Information display method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses an information display method and device, equipment and a storage medium. The method comprises the steps of displaying a business product and a transaction score for the business product in a product transaction application; the transaction score is determined based on the comment data of the business product, and displaying a detail viewing control of the transaction score; and in response to a trigger operation for the detail viewing control, displaying score change trend information for the transaction score. The score change trend information is used for representing the change trend of the transaction emotion of the transaction object for the business product, and the change trend of the transaction emotion is determined based on the emotion feedback for the business product in the comment data of the business product. According to the application, the association degree between the displayed information of the business product and the actual transaction decision can be improved, and the guidance accuracy of the displayed information to the purchaser is improved.

Description

Technical Field

[0001] The present application relates to the field of Internet technology, and in particular to an information display method, device, equipment and storage medium. Background Art

[0002] Business products refer to products selected by buyers (transaction objects) based on their own risk tolerance or needs. Business products may include at least one of stocks, funds, bonds, warrants, futures, and commodities. In order to guide buyers to choose appropriate business products, product introduction information and comment data of business products will be displayed on the transaction page of business products. In practice, it is found that the information displayed on the transaction page has a low correlation with the actual transaction decision, resulting in low accuracy of the information on the transaction page in guiding buyers. Summary of the invention

[0003] The embodiments of the present application provide an information display method, apparatus, device and storage medium, which improve the correlation between the displayed business product information and actual transaction decisions, and improve the accuracy of the displayed information in guiding buyers.

[0004] An embodiment of the present application provides an information display method, including:

[0005] Displaying business products and transaction scores for the business products in a product transaction application; the transaction scores are determined based on review data of the business products;

[0006] A detail view control that displays the transaction score;

[0007] In response to a trigger operation on the detail viewing control, score change trend information for the transaction score is displayed; the score change trend information is used to characterize the change trend of the transaction sentiment of the transaction object towards the business product, and the change trend of the transaction sentiment is determined based on the emotional feedback on the business product in the review data of the business product.

[0008] An embodiment of the present application provides an information display device, including:

[0009] A first display module, used to display business products and transaction scores for the business products in a product transaction application; the transaction scores are determined based on review data of the business products;

[0010] A second display module, used to display a detail viewing control of the transaction score;

[0011] The third display module is used to display the score change trend information for the transaction score in response to the trigger operation of the detail viewing control; the score change trend information is used to characterize the change trend of the transaction sentiment of the transaction object towards the business product, and the change trend of the transaction sentiment is determined based on the emotional feedback on the business product in the review data of the business product.

[0012] On one hand, an embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0013] On one hand, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0014] On the one hand, an embodiment of the present application provides a computer program product, including a computer program, which implements the steps of the above method when executed by a processor.

[0015] In this application, by deeply mining and analyzing the comment data of the business product, the transaction score for the business product and the score change trend information for the transaction score are obtained, and the transaction score of the business product and the score change trend information for the transaction score are displayed in the transaction application, so as to improve the information richness of the displayed business product. The transaction score can reflect the transaction sentiment of the transaction object for the business product in real time, and the score change trend information can reflect the change trend of the transaction sentiment of the transaction object for the business product over a period of time, that is, the transaction score and the score change trend information are highly correlated with the actual transaction decision, and the transaction score and the score change trend information can be used as the transaction reference information of the buyer, that is, to improve the correlation between the information of the displayed business product and the actual transaction decision. In other words, by converting the comment data into easy-to-understand transaction reference information, it is conducive to the accurate grasp and real-time early warning of market sentiment (transaction sentiment), and improve the accuracy of the displayed information in guiding the buyer. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram of an information display system provided by the present application;

[0018] Figure 2 It is an interactive schematic diagram of an information display method provided by the present application;

[0019] Figure 3 It is a flowchart of an information display method provided by the present application;

[0020] Figure 4 This is a schematic diagram of an interface provided by the application that displays transaction ratings and rating change trend information;

[0021] Figure 5 It is another interface schematic diagram provided by the present application for displaying transaction ratings and rating change trend information;

[0022] Figure 6 It is a flowchart of another information display method provided by the present application;

[0023] Figure 7 It is a flowchart of obtaining standard review data provided by this application;

[0024] Figure 8 It is a flowchart of another information display method provided by the present application;

[0025] Fig. 9 It is a flow chart of generating original warning content provided by this application;

[0026] Fig.10 It is a flow chart of generating aggregated warning content provided by this application;

[0027] Fig.11 is a structural schematic diagram of an information display device provided in an embodiment of the present application;

[0028] Fig.12 It is a structural diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] First, the nouns involved in this application are introduced:

[0031] (1) Business products: can include investment products and non-investment products; investment products can refer to products that can provide regular or non-regular returns to the purchaser after purchase, and investment products can include stocks, funds, bonds, warrants, futures, etc. Non-investment products can refer to products that can be used after purchase, and non-investment products can include daily necessities, school supplies, work supplies, travel supplies, etc.

[0032] (2) Transaction score: It is determined based on the review data of the business product, that is, the transaction score can be determined based on the emotional feedback of the transaction object on the business product in the first time period; the transaction score can be used to reflect the transaction sentiment of the transaction object on the business product in the first time period (that is, real-time transaction sentiment), and the transaction score can also be called the sentiment score. For example, the first time period can be the current week, the current day, etc.

[0033] Among them, trading sentiment can include positive trading sentiment and negative trading sentiment. For example, when the business product is an investment product, positive trading sentiment can refer to the feedback from the transaction object that the business product has a relatively high rate of return and relatively small volatility, etc.; negative trading sentiment can refer to the feedback from the transaction object that the business product has a relatively low rate of return and relatively large volatility, etc. When the business product is a non-investment product, positive trading sentiment can refer to the feedback from the transaction object that the user experience of the business product is relatively good, etc.; negative trading sentiment can refer to the feedback from the transaction object that the user experience of the business product is relatively poor, etc.

[0034] Among them, when the business product is an investment product, the emotional feedback may include return feedback, risk feedback, fund manager evaluation, etc.; when the business product is a non-investment product, the emotional feedback may include user experience feedback, cost-effectiveness feedback, etc. for the business product.

[0035] (3) Rating change trend information: used to characterize the changing trend of the transaction object's trading sentiment towards the business product, that is, the rating change trend information reflects the trading sentiment of the transaction object in the first time period, relative to the changing trend of the trading sentiment of the transaction object in the second time period; the second time period is the time before the first time period.

[0036] Among them, the change trend of trading sentiment may include turning to positive trading sentiment, turning to negative trading sentiment, maintaining positive trading sentiment and maintaining negative trading sentiment, etc.; turning to positive trading sentiment may mean that the trading object turns from negative trading sentiment to positive trading sentiment, and turning to positive trading sentiment may be called a strong sentiment. Turning to negative trading sentiment may mean that the trading object turns from positive trading sentiment to negative trading sentiment, and turning to negative trading sentiment may be called a weak sentiment. Maintaining positive trading sentiment may mean that the trading sentiment of the trading object does not change, and it remains positive trading sentiment from the second time period to the first time period; maintaining negative trading sentiment may mean that the trading sentiment of the trading object does not change, and it remains negative trading sentiment from the second time period to the first time period.

[0037] In order to facilitate a clearer understanding of the present application, the information display system implementing the present application is first introduced. Figure 1 As shown, the information display system includes a server and a terminal cluster, and the terminal cluster may include one or more terminals, and the number of terminals is not limited here. Figure 1 As shown, taking the terminal cluster including 4 terminals as an example, the terminal cluster can specifically include a first terminal, a second terminal, a third terminal, and a fourth terminal; it can be understood that the first terminal, the second terminal, the third terminal, and the fourth terminal can all be connected to the server through a network connection, so that each terminal can exchange data with the server through the network connection.

[0038] It is understandable that the server can be an independent physical server, a server cluster or distributed system composed of at least two physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud knowledge base, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery network (Content Delivery Network, CDN), as well as big data and artificial intelligence platforms.

[0039] The terminal may specifically refer to a vending terminal, a vehicle-mounted terminal, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a speaker with a screen, a smart TV, a smart watch, etc., but is not limited thereto. Each terminal and the server may be directly or indirectly connected via wired or wireless communication.

[0040] Among them, any terminal can install and run a product transaction application, which can be at least one of a shopping application, a financial management application, an investment application, and a payment application. The terminal can display product introduction information, comment data, transaction scores, and score change trend information for transaction scores of business products on the transaction page of product business products. The product transaction application can be an independent application, a web application, a mini-program in a host application, etc.

[0041] Among them, the server can be used to provide back-end services for product transaction applications. For example, the server can be used to determine the transaction score for the business product based on the review data of the business product; generate score change trend information for the transaction score, and send the transaction score and score change trend information to the terminal so that the terminal can display the transaction score and score change trend information, which is conducive to guiding buyers to choose suitable business products.

[0042] At present, in order to guide buyers to choose suitable business products, static data such as product introduction information and comment data of business products will be displayed on the transaction page of business products. In practice, it is found that the information displayed on the transaction page is relatively simple, and the comment data is not deeply mined and analyzed, resulting in a low correlation between the displayed content and the actual purchase decision of the business product, resulting in a low accuracy of the information on the transaction page in guiding buyers.

[0043] Based on this, this application proposes an information display method. Figure 2 In the example, the business product is an investment product, and the investment product is business product a, which is a fund; Figure 1 Any terminal (taking the first terminal as an example for explanation) and the server can be used to implement Figure 2 The first terminal has a product transaction application installed therein, and the server may provide backend services for the product transaction application.

[0044] Specifically, the method includes: the first terminal detects a start operation for a product transaction application, and can start the product transaction application. When an add operation for adding business product a to the self-selected list is detected, the terminal can add business product a to the self-selected list, generate a view request for business product a, and the view request is used to request to obtain the transaction score and score change trend information of business product a, and send the view request to the server. The server can obtain the comment data of business product a, identify the comment data of business product a, obtain the transaction score for business product a, and the score change trend information for emotional comments, and send the transaction score and score change trend information to the first terminal. For example, the server can determine the transaction score of business product a based on the emotional feedback in the comment data of business product a on September 23, and determine the score change trend information for the transaction score based on the emotional feedback in the comment data of business product a from September 13 to September 23. The specific process of determining the transaction score and the score change trend information can be described below.

[0045] The self-selected list may include business products that the purchaser is interested in, that is, the business products in the self-selected list are added by the purchaser.

[0046] After receiving the transaction score and the score change trend information, the first terminal can display a transaction page 21a, which includes a self-selected list, which includes business product a, the transaction score of business product a, the detail viewing control 22a, the generation time of the transaction score, the net value and the net value growth rate. The transaction score is 4 points, and the generation time of the transaction score is September 23. The net value can refer to the total assets of business product a divided by the total shares, that is, 5.4280 yuan; the net value growth rate can refer to the growth rate of the net value of business product a within a certain period of time, that is, 10.23%. The detail viewing control 22a can refer to a control for viewing the score change trend information of business product a. The first terminal detects a trigger operation for the detail viewing control 22a, and can display a detail page 23a. The detail page 23a can include the score change trend information for the transaction score, and the score change trend information includes a strong sentiment, that is, the transaction sentiment of the transaction object for business product a turns to a positive transaction sentiment.

[0047] The trigger operation may be any one of a click operation, a press operation, a voice input, and a slide operation.

[0048] It should be noted that the above-mentioned transaction score and score change trend information can also be obtained by the terminal, and the transaction score and score change trend information can also be displayed on the server, which is not limited in this application.

[0049] In summary, the above transaction score can reflect the transaction sentiment of the transaction object for business product a on September 23 in real time, that is, the above transaction score can reflect the real-time transaction sentiment of the transaction object for business product a in real time. The score change trend information can reflect the change trend of the transaction object's transaction sentiment for business products from September 13 to September 23, that is, the transaction score and the score change trend information are highly correlated with the actual transaction decision, and the transaction score and the score change trend information can be used as transaction reference information for buyers. In other words, by converting the comment data into easy-to-understand transaction reference information, it is conducive to accurately grasp the market sentiment (transaction sentiment) and real-time warning, and improve the accuracy of the displayed information in guiding buyers. For example, when a buyer plans to buy a fund, it is found in the self-selected list that the change trend of the transaction sentiment of the transaction object (investor) for business product a turns to positive trading sentiment, and the transaction score of business product a is also relatively high. The buyer is more inclined to invest in business product a, that is, this information can assist the buyer to make a more confident investment decision.

[0050] For further information, see Figure 3 , is a flow chart of an information display method provided in an embodiment of the present application, the method is executed by a computer device, and the computer device includes Figure 1 At least one of any terminal and server shown. Specifically, the terminal is installed and running a product transaction application, the method can be executed by the product transaction application of the terminal, and the server can be used as a background server of the product transaction application in the terminal to provide back-end services for the product transaction application installed and running in the terminal. Figure 3 As shown, the method may include the following steps:

[0051] S101. Displaying business products and transaction scores for the business products in a product transaction application; the transaction scores are determined based on review data of the business products.

[0052] In this application, the computer device can display the business product and the transaction score for the business product in the product transaction application, which is helpful for the buyer to understand the transaction sentiment of the transaction object towards the business product based on the transaction score, and is helpful for guiding the buyer to choose a suitable business product.

[0053] The transaction score reflects the transaction sentiment of the transaction object towards the business product in the first time period, that is, the higher the transaction score, the more positive the transaction sentiment of the transaction object towards the business product in the first time period; conversely, the lower the transaction score, the more negative the transaction sentiment of the transaction object towards the business product in the first time period. The transaction score can be represented by at least one of numbers, symbols, and text. In this application, the use of numbers to represent the transaction score is used as an example for explanation. The transaction score can be 5 points, 10 points, 100 points, etc.

[0054] The business product may refer to the business product that the purchaser is interested in in the product transaction application, and the business product that is interested in may refer to the business product that has been purchased or added to the list of interested products, and the list of interested products may refer to the shopping cart, the list of selected products, etc. Alternatively, the business product may refer to any business product in the product transaction application.

[0055] The transaction object may refer to a user who has purchased a business product. The comment data of a business product may refer to the data obtained from the comment discussion area of ​​the business product, or the comment data may be obtained from news reports, or the comment data may be obtained from the research report opinions issued by the transaction institution.

[0056] S102: Display the details view control of the transaction score.

[0057] In the present application, the computer device may display the detail viewing control of the transaction score, and the sentiment viewing control may refer to a control for viewing the score change trend information of the transaction score.

[0058] S103. In response to a trigger operation on the above-mentioned detail viewing control, display the score change trend information for the above-mentioned transaction score; the above-mentioned score change trend information is used to characterize the change trend of the transaction object's transaction sentiment towards the above-mentioned business product, and the change trend of the above-mentioned transaction sentiment is determined based on the emotional feedback on the above-mentioned business product in the review data of the above-mentioned business product.

[0059] In the present application, when a trigger operation for the detail viewing control is detected, the score change trend information for the transaction score is displayed in response to the trigger operation for the above-mentioned detail viewing control. This helps the buyer understand the changing trend of the transaction object's transaction sentiment for the business product over a period of time, and can serve as transaction reference information for the buyer, which is helpful in assisting the buyer to make reasonable transaction decisions.

[0060] Among them, the score change trend information can be displayed in the transaction page, or the score change trend information can be displayed in the details page, the details page can be a sub-page of the transaction page, the details page can be displayed on the upper layer of the transaction page, and the size of the details page can be less than or equal to the size of the transaction page.

[0061] For example, Figure 4 As shown, taking business product B as an example, business product B is a fund, and the computer device can display the transaction page 41a of business product B, which includes a self-selected list, which includes business product B, the transaction score of business product B, the detail viewing control 42a, the generation time of the transaction score, the net value and the net value growth rate. The transaction score is 2 points, and the generation time of the transaction score is September 24. The net value can refer to the total assets of business product B divided by the total shares, which is 2.23 yuan; the net value growth rate can refer to the growth rate of the net value of business product B within a certain period of time, which is -5.23%. The detail viewing control 42a may refer to a control for viewing the score change trend information of the business product b. The first terminal detects the trigger operation for the detail viewing control 42a and may display the detail page 43a. The detail page 43a may include the score change trend information for the transaction score. The score change trend information indicates that the transaction object's sentiment change trend for the business product is a weakening sentiment, that is, the transaction object's transaction sentiment for the business product b turns to a positive transaction sentiment. The detail page 43a also includes the transaction score of the business product b. Since the transaction score of the business product b is relatively low and the change trend of the transaction sentiment is a weakening sentiment, it indicates that the transaction sentiment of the transaction object of the business product b has significantly deteriorated. That is, it indicates that the recent rate of return of the business product a is relatively low and there is a large fluctuation, that is, there is a certain market risk for the business product a, which can avoid losses caused by the purchase of the business product b by the purchaser, that is, it is helpful to assist the purchaser to predict the potential risks of the business product b in advance so as to adjust the investment plan in time.

[0062] In one embodiment, when the change trend of the transaction sentiment of the transaction object for the above-mentioned business product meets the warning condition, the original warning content about the change trend of the transaction sentiment is displayed; the original warning content is used to prompt the transaction object that the transaction sentiment for the business product has changed, and in response to the trigger operation for the above-mentioned original warning content, the score change trend information for the above-mentioned transaction score is displayed. By displaying the original warning content, it is helpful to prompt the buyer to pay attention to the change trend of the transaction sentiment of the business product in a timely manner to avoid transaction losses.

[0063] For example, Figure 5As shown, taking business product C as an example, business product C is a fund. When the trend of the trading sentiment of business product C turns to negative trading sentiment, the computer device can display the trading page 51a of business product C, which includes a self-selected list, which includes business product C, the trading score of business product C, the generation time of the trading score, the net value, the net value growth rate and the original warning content 52a. The trading score is 2.5 points, and the generation time of the trading score is September 24. The net value can refer to the total assets of business product C divided by the total shares, which is 4.4280 yuan; the net value growth rate can refer to the growth rate of the net value of business product C in a certain period of time, which is -6.23%. The original warning content 52a is: the recent trading sentiment of business product c has changed, please pay attention to it in time and check it out. When the trigger operation for the original warning content 52a is detected, the details page 53a can be displayed. The details page 53a can include the score change trend information for the transaction score. The score change trend information indicates that the trading object's sentiment change trend for the business product is a weakening sentiment, that is, the trading sentiment of the trading object for business product c turns to a positive trading sentiment. The details page 53a also includes the transaction score of business product c. By displaying the original warning content, it is helpful to prompt the buyer to adjust the investment strategy of business product c in time to avoid trading losses.

[0064] In one embodiment, when the change trend of the transaction sentiment of the transaction object for the above-mentioned business product meets the warning condition, the computer device can display the reason why the change trend of the transaction sentiment of the business product meets the warning condition.

[0065] Among them, the reason why the trend of changes in the trading sentiment of the business product meets the early warning condition can be determined based on the comment data of the business product. When the trend of changes in the trading sentiment of the transaction object for the above business product is turning to negative trading sentiment, the reason why the trend of changes in the trading sentiment of the business product meets the early warning condition can be that the rate of return of the business product does not meet expectations, the use effect does not meet expectations, etc. When the trend of changes in the trading sentiment of the transaction object for the above business product is turning to positive trading sentiment, the reason why the trend of changes in the trading sentiment of the business product meets the early warning condition can be that the rate of return of the business product exceeds expectations, the use effect exceeds expectations, etc.

[0066] For example, Figure 5As shown, the details page 53a also includes the reason 54a that the changing trend of the trading sentiment of the business product meets the warning conditions, and the reason 54a is: the performance of the heavily-weighted stocks is lower than expected, and the comment mention rate is 90%, that is, the heavily-weighted stocks refer to funds that account for a large proportion of funds in the investor's investment portfolio and have a significant impact on investment returns. The comment mention rate can refer to the ratio between the number of comment data that mentions reason 54a and the total number of comment data for the business product.

[0067] Among them, the warning conditions may include one or more of the following: 1. The trend of trading sentiment changes to positive trading sentiment; 2. The trend of trading sentiment changes to negative trading sentiment. 3. The trading sentiment is negative trading sentiment, and the duration is greater than the duration threshold; 4. The trading sentiment is negative trading sentiment, and the speed of trading sentiment change is greater than the speed threshold.

[0068] In one embodiment, the display of the original warning content about the change trend of the transaction sentiment includes: the computer device can display the original warning content about the change trend of the transaction sentiment according to the warning level; wherein the warning level is determined according to the transaction score of the business product. By dynamically displaying the original warning content about the business product based on the warning level, it is conducive to improving the display flexibility and diversity of the original warning content.

[0069] Among them, the warning levels include yellow warning, orange warning and red warning, that is, yellow warning is the lowest warning level, red warning is the highest warning level, orange warning is higher than yellow warning and lower than red warning.

[0070] In one embodiment, the computer device can determine the original warning content according to the warning level, and display the original warning content according to the display mode, that is, different original warning contents are displayed for different warning levels, which is helpful to remind buyers to pay attention to risky business products in time.

[0071] In one embodiment, when the speed of change of trading sentiment is greater than the first speed value and the duration is greater than the first duration, it indicates that the trading sentiment has initially fluctuated, and it is determined that the changing trend of the trading sentiment of the transaction object for the above-mentioned business product meets the warning conditions, and the warning level is yellow warning, and the first warning content is used as the original warning content of the business product.

[0072] Among them, the first speed value and the first duration can be preset.

[0073] For example, the first speed value may be 0.15, the first duration is 1 day, the trading sentiment change rates on March 29 and 30 are 0.16 and 0.17 respectively, the trading sentiment change rate is greater than 0.15, and the duration is greater than 1 day, it is determined that the change trend of the trading object's trading sentiment for the above-mentioned business product meets the warning conditions, and the warning level is yellow warning, and the first warning content is used as the original warning content of the business product. The first warning content may be: the trading sentiment of the business product fluctuates, and attention is recommended.

[0074] In one embodiment, when the absolute value of the difference in trading sentiment changes is greater than the first difference and the duration is greater than the second duration, or the absolute value of the trading sentiment change speed is greater than the second speed value, it indicates that the trading sentiment has changed significantly, and it is determined that the change trend of the trading sentiment of the transaction object for the above-mentioned business product meets the warning conditions, and the warning level is orange warning, and the second warning content is used as the original warning content of the business product.

[0075] Among them, the first difference, the second duration, and the second speed value can be preset, the second duration can be greater than the first duration, and the second speed value can be greater than the first speed value. For example, if the first difference is 0.15, the second duration is 2 days, and the first difference is 0.15, the second warning content can be: the trading sentiment of the business product has weakened, and it is recommended to pay close attention.

[0076] In one embodiment, when the absolute value of the difference in trading sentiment change is greater than the second difference, and the duration is greater than the third duration, or the absolute value of the speed of trading sentiment change is greater than the third speed value, it indicates that the trading sentiment has changed drastically, and it is determined that the changing trend of the trading sentiment of the trading object for the above-mentioned business product meets the warning conditions, and the warning level is red warning, and the third warning content is used as the original warning content of the business product.

[0077] Among them, the second difference, the third duration, and the third speed value can be preset, the third duration can be greater than the second duration, the third speed value can be greater than the second speed value, and the second difference is greater than the first difference. For example, the first difference is 0.25, the second duration is 3 days, and the second speed value is 0.35. The third warning content can be: the trading sentiment of the business product has weakened significantly, and it is recommended to deal with it in time.

[0078] In one embodiment, when the changing trend of the transaction sentiment of the above-mentioned transaction object towards the above-mentioned business product turns to positive transaction sentiment, it indicates that the transaction sentiment of the transaction object towards the business product continues to improve, and the computer device can determine that the changing trend of the transaction sentiment of the above-mentioned transaction object towards the above-mentioned business product meets the early warning conditions; this is conducive to buyers seizing trading opportunities for business products.

[0079] In one embodiment, when the changing trend of the trading sentiment of the above-mentioned trading object towards the above-mentioned business product turns to negative trading sentiment, it indicates that the trading sentiment of the trading object towards the business product has significantly deteriorated. The computer device can determine that the changing trend of the trading sentiment of the above-mentioned trading object towards the above-mentioned business product meets the early warning conditions, which is conducive to the buyer to understand the market risks in time and avoid greater losses.

[0080] In one embodiment, in response to a trigger operation for the business product, the score change trend information for the transaction score is displayed, including: when M warnings for the business product are detected within a specified time period, the first aggregated warning content corresponding to the M warnings is displayed; M is an integer greater than 1; each warning indicates that the change trend of the transaction sentiment of the transaction object for the business product meets the warning condition, and the M warnings are determined based on the transaction score. In response to a trigger operation for the first aggregated warning content, the score change trend information corresponding to the Mth warning is displayed. By aggregating the warning content corresponding to the warnings of the business product within the specified time period, that is, aggregating the warning content according to the time dimension, it is possible to avoid too many warnings disturbing the purchaser.

[0081] Among them, the Mth warning may refer to the last warning, that is, the most recent warning, and the Mth warning may refer to the warning in which the time interval between the occurrence time and the current time is the minimum time interval among the M warnings.

[0082] For example, the specified time period can be within 12 hours. If a business product triggers an alert M times within 12 hours, and the alert levels corresponding to the M alerts are the same, the first aggregated alert content corresponding to the M alerts is generated. The first aggregated alert content includes the alert content corresponding to the Mth alert and the number of alerts for the 12-hour content. For example, the alert content of the business product at 10:00, 11:00, and 14:00 are: the trading sentiment of the business product has weakened, the trading sentiment of the business product continues to weaken, and the trading sentiment of the business product continues to be weak. The first aggregated alert content is: the trading sentiment of the business product continues to be weak, and 3 alerts have been triggered in the last 4 hours.

[0083] In one example, in response to a trigger operation for the business product, the score change trend information for the transaction score is displayed, including: when the change trend of the transaction sentiment of the transaction object for the K business products in the product list meets the warning condition, the second aggregated warning content corresponding to the K business products is displayed; K is an integer greater than 1, and the K business products include the business product. In response to the trigger operation for the second aggregated warning content, the score change trend information for the transaction score corresponding to the K business products is displayed. Aggregating the warning content of different business products can help reduce the number of warnings, which can avoid disturbing the purchaser with too many warnings.

[0084] For example, when the trading sentiment change trends of the trading objects for pharmaceutical funds d, pharmaceutical funds e, and pharmaceutical funds f in the list of concerned products all meet the warning conditions, such as the trading sentiment change trends of pharmaceutical funds d, pharmaceutical funds e, and pharmaceutical funds f all turn to negative trading sentiment, the computer device can display the second aggregated warning content about pharmaceutical funds d, pharmaceutical funds e, and pharmaceutical funds f. The second aggregated warning content can be: the general sentiment of pharmaceutical theme funds has weakened, including pharmaceutical funds d, pharmaceutical funds e, and pharmaceutical funds f, and it is recommended to pay attention to the overall performance of the pharmaceutical sector.

[0085] In this application, the transaction score can reflect the transaction object's transaction sentiment towards the business product in real time, and the score change trend information can reflect the change trend of the transaction object's transaction sentiment towards the business product over a period of time, that is, the transaction score and the score change trend information are highly correlated with the actual transaction decision, and the transaction score and the score change trend information can be used as transaction reference information for buyers, that is, to improve the correlation between the displayed business product information and the actual transaction decision. In other words, by converting the review data into easy-to-understand transaction reference information, it is conducive to accurately grasping the market sentiment (transaction sentiment) and real-time early warning, and improving the accuracy of the displayed information in guiding buyers.

[0086] For further information, see Figure 6 , is a flow chart of an information display method provided in an embodiment of the present application, the method is executed by a computer device, and the computer device includes Figure 1 At least one of any terminal and server shown. Specifically, the terminal is installed and running a product transaction application, the method can be executed by the product transaction application of the terminal, and the server can be used as a background server of the product transaction application in the terminal to provide back-end services for the product transaction application installed and running in the terminal. Figure 6 As shown, the method may include the following steps:

[0087] S201. Obtain review data of business products in a product transaction application.

[0088] In the present application, the computer device may obtain comment data of a business product in a product transaction application, and the comment data may be input by a transaction object of the business product.

[0089] S202: Determine a transaction score for the business product based on the review data of the business product.

[0090] In the present application, the computer device can determine the transaction score of the business product based on all the review data of the business product, or the computer device can determine the transaction score of the business product based on the review data of the business product at the first time. The transaction score can reflect the transaction sentiment of the transaction object towards the business product in real time, that is, the correlation between the transaction score and the actual transaction decision is relatively high, which is conducive to using the transaction score as transaction reference information for the buyer.

[0091] In one embodiment, the above-mentioned determination of the transaction score for the above-mentioned business product based on the comment data of the above-mentioned business product includes: the computer device can obtain the comment data with a release time within the first time period from the comment data of the above-mentioned business product; obtain the segmentation type corresponding to each segmentation in the comment data within the above-mentioned first time period, and the segmentation type can include character type, number type, time type, product term type, transaction term type, average type, etc. Since different transaction objects have different expressions for the same segmentation type, the computer device can standardize the corresponding segmentation in the comment data within the above-mentioned first time period according to the segmentation type corresponding to each segmentation, and obtain the standard comment data within the above-mentioned first time period; the standard comment data can refer to the comment data after each segmentation is standardized (unified). Extract the transaction sentiment segmentation from the standard comment data within the above-mentioned first time period, and determine the transaction score for the above-mentioned business product according to the transaction sentiment segmentation corresponding to the standard comment data within the above-mentioned first time period. By standardizing the comment data based on the segmentation type, it is conducive to improving the accuracy of obtaining the transaction score.

[0092] It should be noted that the first time period can be determined based on the current time. For example, if the current time is September 25, the first time period may refer to 00:00-23:59 on September 25. In particular, if the computer device has not obtained the review data of the business product on September 25, the transaction score on September 24 can be used as the transaction score on September 25.

[0093] For example, Figure 7 As shown, the standardization process for review data of business products may include the following steps:

[0094] S71. Data input; the computer device can obtain the comment data of the business product from the product discussion area of ​​the business product through the incremental collection mechanism, add the collected comment data to the original text queue, read the comment data from the original text queue at a preset time interval through the data distributor, and send the read comment data to the basic text processor. The real-time nature of the comment data can be ensured through the incremental collection mechanism, and repeated processing of the comment data can be avoided, thereby avoiding excessive consumption of processing resources.

[0095] Among them, the incremental collection mechanism may refer to collecting newly added comment data in the product discussion area (ie, comment data that has not been collected), that is, only the newly added comment data needs to be processed, which can save processing resources.

[0096] The preset time interval may refer to 4 hours, that is, a batch of comment data is read from the original text queue every 4 hours, and a batch of comment data may refer to multiple comment data.

[0097] S72. Normalize text: Obtain the segmentation type corresponding to the segmentation in the comment data through the basic text processor, and normalize the segmentation of character type, that is, convert the segmentation of character type into full-width characters or half-width characters. Standardize the segmentation of number type and time type, such as converting expressions such as "30 million" into "30 million", and converting "fund size of 250 million" in the comment data into "fund size of 250 million". Standardize the segmentation of time type, such as converting "subscription at the end of last month" in the comment data into "subscription on February 29" (assuming the current date is March). The comment data after processing the segmentation of character type, time type and number type is called normalized text, and the normalized text is sent to the professional terminology processor.

[0098] S73, annotating text; using a professional term processor, identify the product belonging to the standardized text to obtain the segmentation of the product term type, identify the transaction term type to obtain the segmentation of the transaction term type, and identify the evaluation type to obtain the segmentation of the evaluation type. The segmentation of the product term type in the standardized text is term-mapped (i.e., term-standardized) to obtain standardized product terms. "Buy GEM E" in the standardized text is mapped to "GEM ETF", (Exchange Traded Fund, ETF) is an exchange-traded fund. The segmentation of the transaction term type in the standardized text is term-mapped to obtain standardized transaction terms, such as "breaking through the previous high" in the standardized text is mapped to "price breakthrough" + "historical high point". The segmentation of the evaluation type in the standardized text is term-mapped to obtain standardized evaluations. "Strong anti-retracement ability" in the standardized text is mapped to "risk control" + "positive evaluation". Step S73 finally obtains the annotated text, and sends the annotated text to the semantic processor.

[0099] S74, data merging: Through the semantic processor, the adjectives or adverbs in the annotated text are term-mapped (i.e., term-standardized), and "explosive growth", "big rise", and "surge" in the annotated text are unified into "substantially rising". The annotated text is feature-annotated, and the "fund manager's timing is accurate" in the annotated text is annotated as {subject: "fund manager", ability type: "timing", evaluation: "positive"}, and the processed word segmentations are merged to obtain the processed comment data, and the processed comment data is sent to the output layer.

[0100] S75. Output standard comment data; through the output layer, the processed comment data is structured and arranged to obtain structured text, and the structured text is quality checked, that is, whether there are non-standardized word segmentations in the structured text. When there are no non-standardized word segmentations in the structured text, the computer device can use the structured text as standard comment data.

[0101] For example, if the comment data is: This ETF has performed well this year. I invested 25,000 in early March and now I have earned 8 points. Although it retreated a bit last Friday, the fund manager has good control and is ready to increase the position. The standard comment data corresponding to this comment data can be:

[0102] In one embodiment, the transaction sentiment segmentation corresponding to the i-th standard comment data in the first time period includes n basic transaction sentiment segmentations and transaction sentiment adverbs corresponding to the n basic transaction sentiment segmentations, i and n are both positive integers, and i is less than or equal to P, P is the number of standard comment data in the first time period; the transaction score for the business product is determined according to the transaction sentiment segmentations corresponding to the standard comment data in the first time period, including: the computer device can determine the basic vocabulary scores corresponding to the n basic sentiment segmentations according to the positive levels corresponding to the n basic transaction sentiment segmentations. According to the transaction sentiment adverbs corresponding to the n basic transaction sentiment segmentations, the emotional impact degree of each transaction sentiment adverb on the corresponding basic transaction sentiment segmentation is determined; the transaction sentiment adverbs corresponding to the basic transaction sentiment segmentations can refer to adverbs used to describe the basic transaction sentiment segmentations. The computer device can determine the basic comment score of the i-th standard comment data according to the basic vocabulary scores and emotional impact degrees corresponding to the n basic sentiment segmentations; and determine the transaction score for the business product according to the basic comment scores corresponding to the P standard comment data in the first time period. By mining standard review data from multiple dimensions such as basic trading sentiment segmentation and trading sentiment adverbs, we can obtain trading scores for trading products. This will help trading scores accurately reflect the market sentiment of trading products and improve the accuracy of trading scores.

[0103] Among them, basic sentiment segmentation words may include transaction behavior category, evaluation category and performance category. The order of positive levels from high to low is: first positive level, second positive level, third positive level, fourth positive level, fifth positive level. The first positive level is strongly positive, the second positive level is generally positive, the third positive level may refer to neutral, the fourth positive level may refer to generally negative, and the fifth positive level may refer to strongly negative.

[0104] Among them, the higher the positive level of the basic emotional segmentation words, the higher the basic vocabulary scores corresponding to the basic emotional segmentation words, and conversely, the lower the positive level of the basic emotional segmentation words, the lower the basic vocabulary scores corresponding to the basic emotional segmentation words.

[0105] Among them, the basic emotional participles with the first positive level include: trading behavior category: heavy position, increasing position, full position; evaluation category: excellent, outstanding, stable; performance category: big rise, skyrocketing, new high; the basic vocabulary score corresponding to the basic emotional participles with the first positive level is 1 point.

[0106] Among them, the basic emotional participles with the second positive level include: trading behavior category: building a position, holding; evaluation category: good, okay, OK; performance category: rising, profitable, making money; the basic vocabulary score corresponding to the basic emotional participles with the second positive level is 0.5 points.

[0107] Among them, the basic emotional participles with the third positive level include: trading behavior category: wait and see; evaluation category: general, ordinary; performance category: flat, sideways; the basic vocabulary score corresponding to the basic emotional participles with the third positive level is 0 points.

[0108] Among them, the basic emotional participles with the fourth positive level include: trading behavior category: reducing positions, waiting and watching; evaluation category: poor, dissatisfied; performance category: decline, loss; the basic vocabulary score corresponding to the basic emotional participles with the fourth positive level is -0.5 points.

[0109] Among them, the basic emotional participles with the fifth positive level include: trading behavior category: clearing positions, cutting losses; evaluation category: garbage, harming people; performance category: plummeting, falling sharply, and being cut in half; the basic vocabulary score corresponding to the basic emotional participles with the fifth positive level is -1.0 points.

[0110] Among them, different transaction sentiment adverbs correspond to different sentiment impact levels. For example, very, especially, and extremely correspond to the first impact value, relatively and slightly correspond to the second impact value, and possibly and perhaps correspond to the third impact value. The third impact value is less than the second impact value, and the second impact value is less than the first impact value. For example, the first impact value is 1.5, the second impact value is 1.2, and the third impact value is 0.8.

[0111] In one embodiment, the basic review score of the i-th standard review data is determined based on the basic vocabulary scores and emotional impact degrees corresponding to the n basic emotional segmentations, including: the computer device can multiply the basic vocabulary scores corresponding to the n basic emotional segmentations by the corresponding emotional impact degrees to obtain the candidate vocabulary scores corresponding to the n basic emotional segmentations; sum the candidate vocabulary scores corresponding to the n basic emotional segmentations to obtain the basic review score of the i-th standard review data. The basic vocabulary scores corresponding to the basic emotional segmentations are weighted by the emotional impact degree, which is conducive to accurately mining the transaction emotions in the review data.

[0112] For example, the computer device may use the following formula (1) to calculate the basic comment score of the i-th standard comment data:

[0113]

[0114] Among them, in formula (1) is the basic comment score of the i-th standard comment data, B a is the basic vocabulary score corresponding to the a-th basic sentiment segmentation word in the i-th standard comment data, V a is the emotional impact degree corresponding to the a-th basic emotional participle in the i-th standard comment data, where a is a positive integer less than or equal to n.

[0115] In one embodiment, the transaction score for the business product is determined based on the basic comment scores corresponding to the P pieces of standard comment data in the first time period, including: the computer device can determine the business characteristics associated with the business product in the i-th piece of standard comment data; based on the business characteristics, the comment credibility of the i-th piece of standard comment data is determined. The computer device can multiply the comment credibility of the i-th piece of standard comment data by the basic comment score to obtain the weighted comment score of the i-th piece of standard comment data; the transaction score for the business product is determined based on the weighted comment scores corresponding to the P pieces of standard comment data in the first time period. By performing a professional analysis on the standard comment data to determine the comment credibility of the standard comment data, and based on the comment credibility, the transaction score of the business product is determined, which can avoid the interference of non-professional professional comment data on the transaction score and improve the accuracy of the transaction score.

[0116] For example, the computer device may use the following formula (2) to calculate the weighted comment score of the i-th standard comment data:

[0117]

[0118] Among them, in formula (2) is the weighted comment score of the i-th standard comment data, Z i is the comment credibility of the i-th standard comment data.

[0119] Among them, business characteristics may include the number of professional terms, the number of data analyses, the completeness of the transaction logic of standard comment data, etc. A professional term corresponds to a professional score of 0.1 points, a data analysis corresponds to a professional score of 0.2 points, and the professional score corresponding to the completeness of the transaction logic is 0.2 points. The sum of all professional scores is used to obtain the comment credibility corresponding to the i-th standard comment data.

[0120] In one embodiment, the transaction score for the business product is determined based on the weighted review scores corresponding to the P pieces of standard review data within the first time period, including: the computer device can average the weighted review scores corresponding to the P pieces of standard review data within the first time period to obtain the transaction score for the business product.

[0121] In one embodiment, the transaction score for the business product is determined according to the weighted comment scores corresponding to the P standard comment data in the first time period, including: the computer device can standardize the weighted comment score of the i-th standard comment data to obtain the standard comment score of the i-th standard comment data; that is, the weighted comment score of the i-th standard comment data is added to the preset score to obtain the standard comment score of the i-th standard comment data, and the preset score can refer to a number that can make the standard comment score greater than 0, such as the preset score can be 3. The computer device averages the standard comment scores corresponding to the P standard comment data in the first time period to obtain the transaction score for the business product. That is, the standard score values ​​corresponding to the P standard comment data are summed to obtain the total standard score value, and the total standard score value is multiplied by P to obtain the transaction score for the business product. By standardizing the weighted comment score, it is beneficial to control the transaction score within a positive value range and improve the accuracy of obtaining the transaction score.

[0122] For example, the computer device may use the following formula (3) to calculate the standard comment score of the i-th standard comment data:

[0123]

[0124] Among them, in formula (3) is the standard comment score of the i-th standard comment data, and g is the preset score, which can be an empirical value.

[0125] For example, the i-th standard comment data is: This fund has performed very well recently, the selection of stocks in the consumer sector with heavy positions is accurate, the positions have been increased three times, and the rate of return has reached 15%. The i-th standard comment data includes basic sentiment segmentation words with the first positive level, namely "heavy positions", "excellent" and "increase positions", and the basic vocabulary score corresponding to each basic sentiment is 1.0 points. The trading sentiment adverb corresponding to "excellent" is "very", so the emotional impact degree corresponding to "excellent" is 1.5, and the basic comment score corresponding to the i-th standard comment data is: 1.5*1.0+1.0+1.0=3.5 points. Based on the number of professional terms in the i-th standard comment data, the number of data analyses, and the integrity of the trading logic, the credibility of the comment corresponding to the i-th standard comment data is determined to be 1.4. The weighted comment score corresponding to the i-th standard comment data is: 1.4*3.5=4.9 points.

[0126] S203, determining the score change trend information for the above transaction score; the above score change trend information is used to characterize the change trend of the transaction object's transaction sentiment towards the above business product, and the change trend of the above transaction sentiment is determined based on the emotional feedback on the above business product in the review data of the above business product.

[0127] In the present application, the computer device can determine the score change trend for the transaction score based on the review data of the business product, or the computer device can determine the score change trend information for the transaction score based on the historical scores corresponding to the Q second time periods of the business product and the transaction scores corresponding to the first time period. The score change trend information is helpful to understand the change trend of the transaction object's transaction sentiment for the business product, realize the conversion of the review data into easy-to-understand transaction reference information, and realize the accurate grasp and real-time warning of the market sentiment (transaction sentiment), and improve the accuracy of the displayed information in guiding the buyer.

[0128] Each historical score is a transaction score determined based on review data of the business product in a corresponding second time period, and the Q second time periods are located before the first time period.

[0129] In one embodiment, the computer device can analyze the changing trend of trading sentiment based on the moving average, and capture the changes in market sentiment through the interactive relationship between MA5 and MA10. (Moving Average, MA) is a moving average, which is used to smooth the trading sentiment fluctuations of the comment data and identify the changing trend of trading sentiment. MA5 is the average trading score of the past 5 days (such as the first average trading score), and MA10 is the average trading score of the past 10 days (such as the second average trading score). If the current time is March 25, the first time period is March 25, and the Q second time periods can refer to each day from March 15 to March 24, that is, one day corresponds to one second time period.

[0130] In one embodiment, the transaction score is determined based on the comment data of the business product in the first time period; the determination of the score change trend information for the transaction score includes: the computer device can obtain the historical scores corresponding to the Q second time periods of the business product; each historical score is a transaction score determined based on the comment data of the business product in the corresponding second time period, and the Q second time periods are located before the first time period. According to the Q second time periods, the historical scores corresponding to the Q second time periods, the transaction score and the first time period, the change trend of the transaction sentiment of the transaction object for the business product in the first time period is determined. According to the change trend of the transaction sentiment corresponding to the first time period, the score change trend information for the transaction score is generated. If the change trend of the transaction sentiment corresponding to the first time period is a turn to positive transaction sentiment, the score change trend information indicating the turn to positive transaction sentiment is generated, and the score change trend information may be: the sentiment becomes stronger. If the change trend of the transaction sentiment corresponding to the first time period is a turn to negative transaction sentiment, the score change trend information indicating the turn to negative transaction sentiment is generated, and the score change trend information may be: the sentiment becomes weaker. By analyzing the changing trend of trading sentiment based on the moving average, that is, analyzing the changing trend of trading sentiment based on the time series (the first time period, Q second time periods), it is helpful to improve the accuracy of the changing trend of trading sentiment.

[0131] The historical scores corresponding to the Q second time periods are calculated based on the last calculation of the changing trend of trading sentiment.

[0132] In one embodiment, the above-mentioned determination of the change trend of the transaction sentiment of the transaction object for the business product in the first time period according to the Q second time periods, the historical scores corresponding to the Q second time periods, the transaction scores and the first time period includes: calculating the decay weight of the corresponding historical scores according to the time intervals between the Q second time periods and the first time period; multiplying the historical scores corresponding to the Q second time periods by the corresponding decay weights to obtain the weighted transaction scores corresponding to the Q second time periods. The computer device can determine the change trend of the transaction sentiment of the transaction object for the business product in the first time period according to the transaction scores and the weighted transaction scores corresponding to the Q second time periods. Since the transaction sentiment of the transaction object has a "recent effect", that is, the transaction score corresponding to the most recent comment data can better represent the current transaction sentiment, by setting the decay weight, the transaction score corresponding to the most recent comment data can occupy a larger weight, which is conducive to improving the authenticity and reference of the change trend of the transaction sentiment.

[0133] Among them, the attenuation weight can refer to an integer less than 1, and there is a negative correlation between the attenuation weight and the time interval corresponding to the second time period, that is, the larger the time interval corresponding to the second time period, the smaller the attenuation weight of the historical score corresponding to the second time period, that is, the smaller the impact of the historical score corresponding to the second time period on the changing trend of trading sentiment; the smaller the time interval corresponding to the second time period, the larger the attenuation weight of the historical score corresponding to the second time period, that is, the greater the impact of the historical score corresponding to the second time period on the changing trend of trading sentiment.

[0134] In one embodiment, the determination of the change trend of the transaction sentiment of the transaction object for the business product in the first time period according to the transaction score and the weighted transaction scores corresponding to the Q second time periods includes: the computer device can average the transaction score and the weighted transaction scores corresponding to the S second time periods to obtain a first average transaction score; that is, sum the transaction score and the weighted transaction scores corresponding to the S second time periods to obtain a first total transaction score, sum the decay weights of the historical scores corresponding to each of the S second time periods with 1 to obtain a first total decay weight, and divide the first total transaction score by the first total decay weight to obtain a first average transaction score. 1 is the decay weight of the transaction score corresponding to the first time period, the S second time periods are consecutively adjacent second time periods among the Q second time periods, and there is a second time period adjacent to the first time period among the S second time periods, and S is a positive integer less than Q. The above-mentioned transaction score and the weighted transaction scores corresponding to the above-mentioned Q second time periods are averaged to obtain a second average transaction score; that is, the transaction score and the weighted transaction scores corresponding to the Q second time periods are summed to obtain a second total transaction score, the decay weights of the historical scores corresponding to the Q second time periods are summed with 1 to obtain a second total decay weight, and the second total transaction score is divided by the first total decay weight to obtain a second average transaction score. Based on the above-mentioned first average transaction score and the above-mentioned second average transaction score, the changing trend of the transaction sentiment of the above-mentioned transaction object for the above-mentioned business product in the above-mentioned first time period is determined. The changing trend of the transaction sentiment is determined by the first average transaction score and the second average transaction score, which is conducive to identifying changes in market sentiment.

[0135] The S second time periods may refer to the S second time periods with the smallest time interval between the first time period and the Q second time periods. For example, the first time period is March 25, and the Q second time periods are each day from March 15 to March 24, that is, one day corresponds to one second time period, and the S second time periods may refer to each day from March 21 to March 24.

[0136] For example, the change of trading sentiment in about a week can usually reflect the short-term market reaction, and 5 trading days just reflect a week; the time span of two weeks can smooth out the short-term fluctuations and reflect a more stable trend of changes in trading sentiment. Therefore, taking S=4 and Q=9 as an example, the computer device can use the following formula (4) to calculate the first average trading score:

[0137] MA5(t)=[Score(t)+Score(t-1)*α+…+Score(t-4)*α 4 ] / [1+

[0138] α+α 2 +α 3 +α 4 ](4)

[0139] Wherein, MA5(t) in formula (4) may refer to the first average transaction score, Score(t) may refer to the transaction score corresponding to the first time period. Taking March 25 as an example, Score(t-1) is the historical score corresponding to March 24, and Score(t-4) is the historical score corresponding to March 21. α is the decay weight of the historical score corresponding to March 24, which can be any integer less than 1, such as 0.98. 4 is the decay weight of the historical score corresponding to March 21. t is March 25, t-1 can refer to March 24, and t-4 is March 21.

[0140] The computer device may use the following formula (5) to calculate the second average transaction score:

[0141] MA10(t)=[Score(t)+Score(t-1)*α+…+Score(t-9)*α 9 ] / [1+

[0142] α+…+α 9 ](5)

[0143] Among them, MA10(t) in formula (5) is the second average transaction score, Score(t-9) is the historical score corresponding to March 15, and α 9 is the decay weight of the historical score corresponding to March 15, and t-9 may refer to March 15.

[0144] For example, suppose the raw sentiment score data of a business product in the last 6 days is as shown in Table 1:

[0145] Table 1:

[0146] date Number of reviews Sentiment score Decay Weight Weighted Transaction Scoring March 25 42 4.2 0.78 3.276 March 26 38 4.5 0.82 3.69 March 27 45 4.1 0.86 3.526 March 28 35 3.8 0.9 3.42 March 29 50 4.3 0.95 4.085 March 30 40 3.9 1 3.9

[0147] Among them, Table 1 includes the number of comments, sentiment scores, decay weights, and weighted transaction scores corresponding to March 25 to March 30. Taking March 30 as an example, the first average transaction score can be: MA5 = (3.9 × 1.0 + 4.3 × 0.95 + 3.8 × 0.90 + 4.1 × 0.86 + 4.5 × 0.82) / (1.0 + 0.95 + 0.90 + 0.86 + 0.82) = (3.9 + 4.085 + 3.42 + 3.526 + 3.69) / 4.53 = 18.621 / 4.53 = 4.11.

[0148] It should be noted that, when new comment data is added in the first time period, the computer device can calculate the weighted comment score corresponding to the new comment, and based on the weighted comment score corresponding to the new comment, update the transaction score corresponding to the first time period to obtain the updated transaction score corresponding to the first time period. The first average transaction score and the second average transaction score are recalculated based on the above formula (4) and formula (5).

[0149] In one embodiment, the above-mentioned determination of the change trend of the transaction sentiment of the transaction object for the business product in the first time period according to the first average transaction score and the second average transaction score includes: the computer device can perform a difference processing on the first average transaction score and the second average transaction score to obtain a difference value of the transaction sentiment change for the transaction score; that is, the difference value of the transaction sentiment change for the transaction score is obtained by subtracting the second average transaction score from the first average transaction score. The difference processing is performed on the transaction sentiment change difference corresponding to the transaction score and the transaction sentiment change difference of the historical score corresponding to the adjacent time period to obtain the transaction sentiment change speed for the transaction score; the adjacent time period is the second time period adjacent to the first time period in the Q second time periods. The computer device can determine the change trend of the transaction sentiment of the transaction object for the business product in the first time period according to the transaction sentiment change difference and the transaction sentiment change speed for the transaction score. The change trend of the transaction sentiment is determined by the transaction sentiment change speed and the transaction sentiment change difference, so as to determine the change trend of the transaction sentiment from multiple dimensions, which is conducive to reflecting the change trend of the real market sentiment through the change trend of the transaction sentiment.

[0150] For example, the computer device may use the following formula (6) to calculate the trading sentiment change difference:

[0151] DIF(t)=MA5(t)-MA10(t)(6)

[0152] Where DIF(t) in formula (6) is the difference in trading sentiment change for trading scores, and the trading sentiment change speed can be calculated using the following formula (7):

[0153] ΔDIF(t)=DIF(t)-DIF(t-1)(7)

[0154] Among them, ΔDIF(t) in formula (7) is the speed of change of trading sentiment, and DIF(t-1) is the difference in trading sentiment changes of historical scores corresponding to adjacent time periods. For example, if t is March 25, DIF(t-1) is the difference in trading sentiment changes of historical scores corresponding to March 24.

[0155] Wherein, (Difference, DIF) is the difference in trading sentiment change, which is used to calculate the difference between the two moving averages, that is, the two moving averages refer to the line segment used to reflect the first average trading score and the line segment reflecting the second average trading score. (Delta Difference, ΔDIF) is the trading sentiment change speed, which reflects the speed of change of trading sentiment, that is, the speed of change of trading sentiment in unit time. When the first time period is a certain day, the unit time can refer to 1 day. When the first time period can be d hours, the unit time can refer to d hours, and d can be greater than an integer.

[0156] In one embodiment, the above-mentioned determination of the change trend of the transaction sentiment of the transaction object for the business product in the first time period according to the transaction sentiment change difference and the transaction sentiment change speed for the transaction score includes: when the absolute value of the transaction sentiment change speed is greater than the first speed threshold, the transaction sentiment change difference corresponding to the transaction score is compared with the change threshold, and the transaction sentiment change speed is compared with the second speed threshold; the first speed threshold is greater than the second speed threshold. When the transaction sentiment change difference for the transaction score is greater than the change threshold, and the transaction sentiment change speed is greater than the second speed threshold, it will turn to positive transaction sentiment, which is determined as the change trend of the transaction sentiment of the transaction object for the business product in the first time period. By identifying the moment when the transaction sentiment of the business product turns to positive transaction sentiment, it is helpful to prompt the buyer to pay attention to the emotional changes of the business product in time and seize the favorable opportunity for the transaction. When the transaction sentiment change difference for the transaction score is less than the change threshold, and the transaction sentiment change speed is less than the second speed threshold, it will turn to negative transaction sentiment, which is determined as the change trend of the transaction sentiment of the transaction object for the business product in the first time period. By identifying the moment when the trading sentiment of a business product turns to negative trading sentiment, it is helpful to remind buyers to pay attention to the emotional changes of the business product in a timely manner, trade cautiously, and avoid trading risks. By analyzing the changing trend of trading sentiment based on the moving average analysis method, it is possible to effectively filter out the interference of short-term fluctuations and capture truly meaningful changes in trading sentiment. By setting reasonable attenuation weights and threshold parameters, it is possible to avoid excessive false positives while maintaining sensitivity.

[0157] Among them, the change threshold, the first speed threshold, and the second speed threshold can be pre-set. For example, the first speed threshold can belong to the numerical range (0.05, 0.3). When the change threshold and the second speed threshold are 0, the turn to negative trading sentiment and the turn to positive trading sentiment can be called a sentiment inflection point.

[0158] For example, the generation process of score change trend information is shown in Table 2 below:

[0159] Table 2:

[0160]

[0161] Among them, if in Table 2 means if, δ is the first speed threshold, and elif means otherwise.

[0162] In one embodiment, when the computer device is a server, the server can send the transaction score and the score change trend information to the terminal corresponding to the product transaction application, and the terminal is used to display the business product and the transaction score in the product transaction application, and display the score change trend information for the transaction score in response to a trigger operation for the business product. The transaction score and the score change trend information are sent to the terminal corresponding to the product transaction application by the server so that the terminal can display the transaction score and the score change trend information, and the transaction score and the score change trend information are helpful to assist buyers in making reasonable transaction decisions.

[0163] In one embodiment, when the change trend of the transaction sentiment of the transaction object for the business product meets the early warning condition, the early warning level for the business product is determined according to the first average transaction score and the second average transaction score; according to the early warning level, the original early warning content about the change trend of the transaction sentiment is generated. Specifically, the first average transaction score and the second average transaction score are subjected to difference processing to obtain the transaction sentiment change difference, and the transaction change difference and the transaction sentiment change difference of the historical score corresponding to the adjacent time period are subjected to difference processing to obtain the transaction sentiment change speed for the transaction score. According to the transaction change difference, the transaction sentiment change speed and the duration, the early warning level for the business product is determined, and according to the early warning level, the original early warning content for the business product is generated. The early warning level and the original early warning content are sent to the terminal. Different early warning contents are generated by different early warning levels, which is conducive to improving the effectiveness of the early warning and prompting the buyer to check the change trend of the transaction sentiment in time.

[0164] In this application, by deeply mining and analyzing the comment data of the business product, the transaction score for the business product and the score change trend information for the transaction score are obtained, and the transaction score of the business product and the score change trend information for the transaction score are displayed in the transaction application, so as to improve the information richness of the displayed business product. The transaction score can reflect the transaction sentiment of the transaction object for the business product in real time, and the score change trend information can reflect the change trend of the transaction sentiment of the transaction object for the business product over a period of time, that is, the transaction score and the score change trend information are highly correlated with the actual transaction decision, and the transaction score and the score change trend information can be used as the transaction reference information of the buyer, that is, to improve the correlation between the information of the displayed business product and the actual transaction decision. In other words, by converting the comment data into easy-to-understand transaction reference information, it is conducive to the accurate grasp and real-time early warning of market sentiment (transaction sentiment), and improve the accuracy of the displayed information in guiding the buyer.

[0165] In one embodiment, Figure 8As shown, the information display method in the present application may include the following steps:

[0166] S81. Obtain standard comment data. The computer device may obtain comment data of the business product in the first time period from the comment discussion area of ​​the business product, perform text preprocessing, professional vocabulary recognition and abnormal content filtering on the comment data, and obtain standard comment data.

[0167] Among them, text preprocessing can refer to the normalization of character-type segmentation, number-type segmentation, and time-type segmentation in the comment data; professional vocabulary recognition can refer to the normalization of product terminology-type segmentation, transaction terminology-type segmentation, and evaluation-type segmentation.

[0168] Among them, abnormal content filtering processing may refer to obtaining the correlation between comment data and attribute information of business products, treating comment data with a correlation less than a correlation threshold as invalid comment data, that is, filtering out invalid comment data, and no processing is required. Attribute information may refer to product introduction information of business products, such as price, name, etc.

[0169] S82. Obtaining transaction scores; including sentiment feature extraction, calculating transaction scores and time decay processing. Specifically, sentiment feature extraction means that the computer device can extract sentiment features from the standard comment data to obtain transaction sentiment segmentation. Calculating transaction scores may mean that the transaction score for the business product is calculated based on the transaction sentiment segmentation in the standard comment data. Time decay processing means that the historical scores corresponding to the business product in Q second time periods are obtained, and based on the Q second time periods, the decay weights of the historical scores corresponding to the Q second time periods are determined, and the historical scores corresponding to the Q second time periods are multiplied by the corresponding decay weights to obtain the weighted transaction scores corresponding to the Q second time periods.

[0170] S83, obtaining score change trend information; including MA5 calculation, MA10 calculation, difference analysis, and inflection point identification. MA5 calculation may refer to: averaging the weighted transaction scores and transaction scores corresponding to S second time periods to obtain a first average transaction score; MA10 calculation may refer to: averaging the weighted transaction scores and transaction scores corresponding to Q second time periods to obtain a second average transaction score. Difference analysis may refer to: performing difference processing on the first average transaction score and the second average transaction score to obtain a transaction sentiment change difference, performing difference processing on the transaction sentiment change difference corresponding to the above transaction score and the transaction sentiment change difference of the historical score corresponding to the adjacent time period to obtain the transaction sentiment change speed for the above transaction score, and determining the change trend of the transaction sentiment according to the transaction sentiment change difference and the transaction sentiment change speed. Inflection point identification may refer to: when the absolute value of the transaction sentiment change speed is greater than the first speed threshold, and the transaction sentiment change difference is greater than the change threshold, and the transaction sentiment change speed is greater than the second speed threshold, it is determined that the transaction sentiment has an inflection point, that is, the change trend of the transaction sentiment changes from negative transaction sentiment to positive transaction sentiment. When the absolute value of the trading sentiment change speed is greater than the first speed threshold, and the trading sentiment change difference is less than the change threshold, and the trading sentiment change speed is less than the second speed threshold, it is determined that the trading sentiment has an inflection point, that is, the changing trend of the trading sentiment turns from positive trading sentiment to negative trading sentiment.

[0171] S84. The computer device can output the transaction score and generate original warning content based on the changing trend of transaction sentiment.

[0172] It should be noted that for Figure 8 For explanations of the various steps in the embodiment, please refer to the previous embodiments, and the repeated parts will not be repeated.

[0173] In one embodiment, Fig. 9 As shown, the generation process of the original warning content in this application may include the following steps:

[0174] S91. Obtain transaction score. For the specific implementation process of this step, please refer to Figure 8 The implementation process of step S82 in , the repeated parts will not be repeated.

[0175] S92, obtaining score change trend information; the computer device can implement MA5 calculation through the MA5 calculation model to obtain the first average transaction score, implement MA10 calculation through the MA10 calculation model to obtain the second average transaction score, implement difference analysis through the difference analyzer to obtain the difference in trading sentiment change and the speed of trading sentiment change. Implement inflection point identification through the inflection point detector to obtain the change trend of trading sentiment.

[0176] S93, result output; the computer device can determine the warning level for the above-mentioned business product according to the transaction change difference, the speed of change of transaction sentiment and the duration, generate original warning content for the business product according to the warning level, and output the original warning content, that is, the terminal displays the original warning content to prompt the buyer to pay attention to the changing trend of transaction sentiment so as to make appropriate transaction decisions.

[0177] It should be noted that for Fig. 9 For explanations of the various steps in the embodiment, please refer to the previous embodiments, and the repeated parts will not be repeated.

[0178] In one embodiment, Fig.10 As shown, the generation process of the aggregated warning content in the present application can be performed by a warning network, which may include an input layer, a processing layer, and an output layer. The warning network may refer to a neural network, a natural language processing network, etc. The generation process of the aggregated warning content may include the following steps:

[0179] S110. The input layer can be used to input transaction scores and historical scores; specifically, the computer device can input the transaction scores of the business products in the first time period and the historical scores corresponding to Q second time periods into the input layer. The input layer determines the transaction sentiment change difference and the transaction sentiment change speed based on the transaction score and the historical scores corresponding to the Q second time periods through a threshold determination module, obtains a first speed threshold, a change threshold, and a second speed threshold, and inputs the first speed threshold, the change threshold, the second speed threshold, the transaction sentiment change difference, and the transaction sentiment change speed into the processing layer.

[0180] S111. The processing layer can be used for warning level classification, message aggregation processing and display content generation; for example, the warning level classification can refer to: when the trading sentiment change speed is greater than the first speed value and the duration is greater than the first duration, the warning level is determined to be a yellow warning. When the absolute value of the trading sentiment change difference is greater than the first difference and the duration is greater than the second duration, or the absolute value of the trading sentiment change speed is greater than the second speed value, the warning level is determined to be an orange warning. When the absolute value of the trading sentiment change difference is greater than the second difference and the duration is greater than the third duration, or the absolute value of the trading sentiment change speed is greater than the third speed value, the warning level is determined to be a red warning. Message aggregation processing: Based on the warning level, the original warning content of the business product is generated, and based on the original warning content corresponding to each concerned business product in the self-selected list, the original warning content that needs to be aggregated is identified, such as the original warning content corresponding to the same warning level is used as the original warning content that needs to be merged. Display content generation can refer to: generating aggregated warning content according to the original warning content that needs to be aggregated) (i.e., the second aggregated warning content mentioned above).

[0181] S112. The output layer can be used to output aggregated warning content, and based on the change trend of the transaction sentiment of each concerned business product, update the change trend of the original transaction sentiment of each concerned business product in the self-selected list.

[0182] It should be noted that for Fig.10 For explanations of each step, please refer to the previous embodiments, and the repeated parts will not be repeated.

[0183] In summary, by generating aggregated warning content for each business product of concern, it is possible to avoid excessive disturbance to buyers and improve the effectiveness of the warning.

[0184] See also Fig.11 A schematic diagram of the structure of an information display device provided in an embodiment of the present application. Fig.11 , the information display device may include:

[0185] A first display module 1211 is used to display business products and transaction scores for the business products in a product transaction application; the transaction scores are determined based on review data of the business products;

[0186] A second display module 1212, used to display a detail viewing control of the transaction score;

[0187] The third display module 1213 is used to display the score change trend information for the transaction score in response to the trigger operation of the detail viewing control; the score change trend information is used to characterize the change trend of the transaction object's transaction sentiment for the business product, and the change trend of the transaction sentiment is determined based on the emotional feedback on the business product in the review data of the business product.

[0188] Optionally, the third display module 1213 is further configured to display original warning content regarding the change trend of the transaction sentiment when the change trend of the transaction sentiment of the transaction object for the business product meets the warning condition;

[0189] In response to the triggering operation on the original warning content, score change trend information on the transaction score is displayed.

[0190] Optionally, the third display module 1213 is further used to display the original warning content about the change trend of the above trading sentiment according to the warning level;

[0191] Among them, the above warning levels are determined based on the transaction scores for the above business products.

[0192] Optionally, the third display module 1213 is further specifically configured to determine that the trend of change in the transaction sentiment of the transaction object for the business product meets the warning condition when the trend of change in the transaction sentiment of the transaction object for the business product turns to positive transaction sentiment; or

[0193] When the changing trend of the transaction sentiment of the transaction object with respect to the business product turns to negative transaction sentiment, it is determined that the changing trend of the transaction object with respect to the business product meets the early warning condition.

[0194] Optionally, the third display module 1213 is further configured to display the first aggregated warning content corresponding to the M warnings when M warnings are detected for the business product within a specified time period; M is an integer greater than 1; each warning indicates that the change trend of the transaction sentiment of the transaction object for the business product meets the warning condition, and the M warnings are determined based on the transaction score;

[0195] In response to the triggering operation on the above-mentioned first aggregated warning content, the score change trend information corresponding to the Mth warning is displayed.

[0196] Optionally, the third display module 1213 is further configured to display the second aggregated warning content corresponding to the K concerned business products when the change trends of the transaction sentiments of the transaction object for the K concerned business products in the concerned product list all meet the warning conditions; K is an integer greater than 1, and the K concerned business products include the business product;

[0197] In response to a triggering operation on the second aggregated warning content, score change trend information of the transaction scores respectively corresponding to the K concerned business products is displayed.

[0198] Optionally, the first determining module 1214 is configured to obtain, from the comment data of the business product, comment data with a publishing time within a first time period;

[0199] Obtain the segmentation type corresponding to each segmentation in the comment data within the first time period.

[0200] According to the segmentation types corresponding to the above-mentioned segmentations, the corresponding segmentations in the comment data within the above-mentioned first time period are standardized to obtain the standard comment data within the above-mentioned first time period;

[0201] Extract transaction sentiment segmentation words from the standard review data in the first time period;

[0202] A transaction score for the business product is determined based on the transaction sentiment segmentations corresponding to the standard review data within the first time period.

[0203] Optionally, the transaction sentiment segmentation corresponding to the i-th standard comment data in the first time period includes n basic transaction sentiment segmentations and transaction sentiment adverbs corresponding to the n basic transaction sentiment segmentations, respectively, where i and n are both positive integers, and i is less than or equal to P, where P is the number of standard comment data in the first time period;

[0204] Optionally, the first determination module 1214 is specifically configured to determine the basic vocabulary scores corresponding to the n basic transaction sentiment segmentations according to the positive levels corresponding to the n basic transaction sentiment segmentations;

[0205] According to the transaction sentiment adverbs corresponding to the n basic transaction sentiment participles, the degree of sentiment influence of each transaction sentiment adverb on the corresponding basic transaction sentiment participle is determined;

[0206] Determine the basic comment score of the i-th standard comment data according to the basic vocabulary scores and the emotional impact degrees corresponding to the n basic emotional participles;

[0207] A transaction score for the business product is determined according to the basic review scores corresponding to the P pieces of standard review data within the first time period.

[0208] Optionally, the first determination module 1214 is specifically configured to multiply the basic vocabulary scores corresponding to the n basic emotion participles by the corresponding emotion impact degrees to obtain the candidate vocabulary scores corresponding to the n basic emotion participles;

[0209] The candidate vocabulary scores corresponding to the above n basic sentiment participles are summed up to obtain the basic comment score of the above i-th standard comment data.

[0210] Optionally, the first determination module 1214 is specifically configured to determine the business feature associated with the business product in the i-th standard review data;

[0211] According to the above business characteristics, determine the credibility of the review data of the above-mentioned standard i;

[0212] The comment credibility of the above-mentioned i-th standard comment data is multiplied by the basic comment score to obtain the weighted comment score of the above-mentioned i-th standard comment data;

[0213] A transaction score for the business product is determined based on the weighted review scores corresponding to the P pieces of standard review data within the first time period.

[0214] Optionally, the first determination module 1214 is specifically configured to perform standardization processing on the weighted comment score of the i-th standard comment data to obtain the standard comment score of the i-th standard comment data;

[0215] The standard review scores corresponding to the P pieces of standard review data within the first time period are averaged to obtain a transaction score for the business product.

[0216] Optionally, the second determination module 1215 is used to obtain historical scores corresponding to the Q second time periods of the business product; each historical score is a transaction score determined based on the review data of the business product in the corresponding second time period, and the Q second time periods are located before the first time period;

[0217] Determine a change trend of the transaction sentiment of the transaction object for the business product within the first time period according to the Q second time periods, the historical scores respectively corresponding to the Q second time periods, the transaction scores and the first time period;

[0218] According to the change trend of the transaction sentiment corresponding to the first time period, score change trend information for the transaction score is generated.

[0219] Optionally, the second determination module 1215 is specifically configured to determine the decay weight of the corresponding historical score according to the time intervals between the Q second time periods and the first time period;

[0220] Multiplying the historical scores corresponding to the Q second time periods by the corresponding decay weights to obtain weighted transaction scores corresponding to the Q second time periods;

[0221] According to the transaction score and the weighted transaction scores corresponding to the Q second time periods, a change trend of the transaction sentiment of the transaction object for the business product within the first time period is determined.

[0222] Optionally, the second determination module 1215 is specifically configured to average the transaction score and the weighted transaction scores corresponding to the S second time periods to obtain a first average transaction score; the S second time periods are consecutively adjacent second time periods among the Q second time periods, and there is a second time period adjacent to the first time period among the S second time periods, and S is a positive integer less than Q;

[0223] Averaging the transaction score and the weighted transaction scores corresponding to the Q second time periods to obtain a second average transaction score;

[0224] According to the first average transaction score and the second average transaction score, a change trend of the transaction object's transaction sentiment for the business product during the first time period is determined.

[0225] Optionally, the second determination module 1215 is specifically configured to perform a difference processing on the first average transaction score and the second average transaction score to obtain a transaction sentiment change difference for the transaction score;

[0226] The difference between the transaction sentiment change difference corresponding to the transaction score and the transaction sentiment change difference of the historical score corresponding to the adjacent time period is calculated to obtain the transaction sentiment change speed for the transaction score; the adjacent time period is a second time period adjacent to the first time period among the Q second time periods;

[0227] According to the transaction sentiment change difference and the transaction sentiment change speed for the transaction score, the change trend of the transaction sentiment of the transaction object for the business product in the first time period is determined.

[0228] Optionally, the second determination module 1215 is specifically configured to compare the transaction sentiment change difference corresponding to the transaction score with the change threshold, and compare the transaction sentiment change speed with a second speed threshold when the absolute value of the transaction sentiment change speed is greater than the first speed threshold; the first speed threshold is greater than the second speed threshold;

[0229] When the difference in transaction sentiment change for the transaction score is greater than the change threshold, and the transaction sentiment change speed is greater than the second speed threshold, the transaction sentiment will be turned to positive, and determined as the change trend of the transaction object's transaction sentiment for the business product in the first time period;

[0230] When the difference in transaction sentiment change for the above transaction score is less than the above change threshold, and the transaction sentiment change speed is less than the above second speed threshold, it will turn to negative transaction sentiment and be determined as the change trend of the transaction object's transaction sentiment for the above business product during the above first time period.

[0231] Optionally, the second determination module 1215 is specifically configured to determine a warning level for the business product according to the first average transaction score and the second average transaction score when the change trend of the transaction object's transaction sentiment for the business product meets the warning condition;

[0232] According to the above warning level, generate original warning content about the change trend of the above trading sentiment;

[0233] The above-mentioned warning level and the above-mentioned original warning content are sent to the above-mentioned terminal.

[0234] In this application, by deeply mining and analyzing the comment data of the business product, the transaction score for the business product and the score change trend information for the transaction score are obtained, and the transaction score of the business product and the score change trend information for the transaction score are displayed in the transaction application, so as to improve the information richness of the displayed business product. The transaction score can reflect the transaction sentiment of the transaction object for the business product in real time, and the score change trend information can reflect the change trend of the transaction sentiment of the transaction object for the business product over a period of time, that is, the transaction score and the score change trend information are highly correlated with the actual transaction decision, and the transaction score and the score change trend information can be used as the transaction reference information of the buyer, that is, to improve the correlation between the information of the displayed business product and the actual transaction decision. In other words, by converting the comment data into easy-to-understand transaction reference information, it is conducive to the accurate grasp and real-time early warning of market sentiment (transaction sentiment), and improve the accuracy of the displayed information in guiding the buyer.

[0235] See also Fig.12 , is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. Fig.12 As shown, the computer device may refer to the above-mentioned identification device, and the above-mentioned computer device 1000 may refer to a terminal or a server, including: a processor 1001, a network interface 1004 and a memory 1005. In addition, the above-mentioned computer device 1000 may also include: a user interface 1003, and at least one communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. Among them, in some embodiments, the user interface 1003 may include a display screen (DiSPlay), a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or it may be a non-volatile memory (non-volatile MeMory), such as at least one disk storage. The memory 1005 may also be optionally at least one storage device away from the aforementioned processor 1001. As Fig.12 As shown, the memory 1005 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module, and a computer program.

[0236] exist Fig.12 In the computer device 1000 shown, the network interface 1004 can provide network communication functions; the user interface 1003 is mainly used to provide an input interface; and the processor 1001 can be used to call the computer program stored in the memory 1005 to implement the method described above in this application.

[0237] In this application, by deeply mining and analyzing the comment data of the business product, the transaction score for the business product and the score change trend information for the transaction score are obtained, and the transaction score of the business product and the score change trend information for the transaction score are displayed in the transaction application, so as to improve the information richness of the displayed business product. The transaction score can reflect the transaction sentiment of the transaction object for the business product in real time, and the score change trend information can reflect the change trend of the transaction sentiment of the transaction object for the business product over a period of time, that is, the transaction score and the score change trend information are highly correlated with the actual transaction decision, and the transaction score and the score change trend information can be used as the transaction reference information of the buyer, that is, to improve the correlation between the information of the displayed business product and the actual transaction decision. In other words, by converting the comment data into easy-to-understand transaction reference information, it is conducive to the accurate grasp and real-time early warning of market sentiment (transaction sentiment), and improve the accuracy of the displayed information in guiding the buyer.

[0238] It should be understood that the computer device described in the embodiments of the present application can execute the description of the above-mentioned information display method in the corresponding embodiments above, and can also execute the description of the above-mentioned information display device in the corresponding embodiments above, which will not be repeated here. In addition, the description of the beneficial effects of adopting the same method will not be repeated.

[0239] The data collection and processing in this application should be strictly in accordance with the requirements of relevant laws and regulations when applied in practice, and the informed consent or separate consent of the personal information subject should be obtained. Subsequent data use and processing should be carried out within the scope of authorization of laws and regulations and the personal information subject.

[0240] In addition, it should be pointed out here that: the embodiment of the present application also provides a computer-readable storage medium, and the above-mentioned computer-readable storage medium stores a computer program executed by the information display device mentioned above, and the above-mentioned computer program includes program instructions. When the above-mentioned processor executes the above-mentioned program instructions, it can execute the description of the above-mentioned information display method in the corresponding embodiment above, so it will not be repeated here. In addition, the description of the beneficial effects of using the same method will not be repeated. For technical details not disclosed in the computer-readable storage medium embodiment involved in this application, please refer to the description of the method embodiment of this application.

[0241] As an example, the above program instructions may be deployed on a computer device for execution, or deployed on at least two computer devices at one location for execution, or executed on at least two computer devices distributed at at least two locations and interconnected through a communication network. At least two computer devices distributed at at least two locations and interconnected through a communication network may constitute a blockchain network.

[0242] The computer-readable storage medium may be the information display device provided in any of the aforementioned embodiments or the central storage unit of the computer device, such as a hard disk or a memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SMART Media card, SMC), a secure digital (Secure digital, SD) card, a flash memory card (flaSh card), etc. equipped on the computer device. Further, the computer-readable storage medium may also include both the central storage unit of the computer device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0243] The terms "first", "second", etc. in the description, claims, and drawings of the embodiments of the present application are used to distinguish between contents in different media, rather than to describe a specific order. In addition, the terms "including" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, device, product, or equipment that includes a series of steps or units is not limited to the listed steps or modules, but may optionally include steps or modules that are not listed, or may optionally include other step units inherent to these processes, methods, devices, products, or equipment.

[0244] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0245] The data collection and processing in this application should be strictly in accordance with the requirements of relevant laws and regulations when applied in practice, and the informed consent or separate consent of the personal information subject should be obtained. Subsequent data use and processing should be carried out within the scope of authorization of laws and regulations and the personal information subject.

[0246] The embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the description of the above-mentioned information display method and decoding method in the above-mentioned corresponding embodiment, so it will not be repeated here. In addition, the description of the beneficial effects of adopting the same method will not be repeated. For technical details not disclosed in the embodiment of the computer program product involved in this application, please refer to the description of the method embodiment of this application.

[0247] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0248] The method and related apparatus provided by the embodiment of the present application are described with reference to the method flow chart and / or structural diagram provided by the embodiment of the present application. Specifically, each process and / or box in the method flow chart and / or structural diagram, as well as the combination of the processes and / or boxes in the flow chart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable network connection device to generate a machine, so that the instructions executed by the processor of the computer or other programmable network connection device generate instructions for implementing the process in the process. Figure 1 A process or multiple processes and / or structures Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable network-connected device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or structures Figure 1 These computer program instructions can also be loaded onto a computer or other programmable network connected device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process. Figure 1 A flow or multiple flows and / or structures illustrate the steps of the functions specified in one block or multiple blocks.

[0249] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. An information display method, characterized in that: The method comprises: Displaying business products and transaction scores for the business products in a product transaction application; the transaction scores are determined based on review data of the business products; A detail view control that displays the transaction score; In response to a trigger operation on the detail viewing control, score change trend information for the transaction score is displayed; the score change trend information is used to characterize the change trend of the transaction sentiment of the transaction object towards the business product, and the change trend of the transaction sentiment is determined based on the emotional feedback on the business product in the review data of the business product.

2. The method according to claim 1, characterized in that The step of displaying score change trend information for the transaction score in response to the trigger operation for the business product includes: When the change trend of the transaction object's transaction sentiment for the business product meets the warning condition, the original warning content about the change trend of the transaction sentiment is displayed; In response to a triggering operation on the original warning content, score change trend information on the transaction score is displayed.

3. The method according to claim 2, characterized in that The original warning content showing the changing trend of the trading sentiment includes: Display the original warning content about the changing trend of the trading sentiment according to the warning level; The warning level is determined based on the transaction score for the business product.

4. The method according to claim 2, characterized in that The method further comprises: When the change trend of the transaction object's transaction sentiment toward the business product turns to positive transaction sentiment, it is determined that the change trend of the transaction object's transaction sentiment toward the business product meets the warning condition; or When the change trend of the transaction sentiment of the transaction object for the business product turns to negative transaction sentiment, it is determined that the change trend of the transaction sentiment of the transaction object for the business product meets the warning condition.

5. The method according to claim 1, characterized in that The method further comprises: When M warnings are detected for the business product within a specified time period, the first aggregated warning content corresponding to the M warnings is displayed; M is an integer greater than 1; each warning indicates that the change trend of the transaction object's transaction sentiment for the business product meets the warning condition, and the M warnings are determined based on the transaction score; In response to a trigger operation on the first aggregated warning content, score change trend information corresponding to the Mth warning is displayed.

6. The method according to claim 1, characterized in that The method further comprises: When the change trends of the transaction sentiments of the transaction object for K concerned business products in the concerned product list all meet the warning conditions, display the second aggregated warning content corresponding to the K concerned business products; K is an integer greater than 1, and the K concerned business products include the business product; In response to a triggering operation on the second aggregated warning content, score change trend information of the transaction scores respectively corresponding to the K concerned business products is displayed.

7. The method according to claim 1, characterized in that The method further comprises: Acquire, from the comment data of the business product, comment data whose publishing time is within a first time period; Obtain the segmentation type corresponding to each segmentation in the comment data within the first time period, According to the segmentation types corresponding to the respective segmentations, the corresponding segmentations in the comment data within the first time period are standardized to obtain standard comment data within the first time period; Extracting transaction sentiment segmentations from the standard review data within the first time period; A transaction score for the business product is determined according to the transaction sentiment segmentations corresponding to the standard review data within the first time period.

8. The method according to claim 7, characterized in that The transaction sentiment segmentation corresponding to the i-th standard comment data in the first time period includes n basic transaction sentiment segmentations and transaction sentiment adverbs corresponding to the n basic transaction sentiment segmentations, i and n are both positive integers, and i is less than or equal to P, where P is the number of standard comment data in the first time period; The determining of a transaction score for the business product according to the transaction sentiment segmentation corresponding to the standard review data within the first time period includes: Determining the basic vocabulary scores corresponding to the n basic transaction sentiment participles respectively according to the positive levels corresponding to the n basic transaction sentiment participles respectively; Determining the degree of emotional impact of each transaction emotion adverb on the corresponding basic transaction emotion participle according to the transaction emotion adverbs corresponding to the n basic transaction emotion participles; Determine the basic comment score of the i-th standard comment data according to the basic vocabulary scores and the degree of emotional impact respectively corresponding to the n basic emotional participles; A transaction score for the business product is determined according to the basic review scores respectively corresponding to the P pieces of standard review data within the first time period.

9. The method according to claim 8, characterized in that The determining the basic comment score of the i-th standard comment data according to the basic vocabulary scores and the emotional impact degrees respectively corresponding to the n basic emotional participles includes: The basic vocabulary scores corresponding to the n basic emotion participles are multiplied by the corresponding emotion impact degrees to obtain the candidate vocabulary scores corresponding to the n basic emotion participles; The candidate vocabulary scores corresponding to the n basic sentiment participles are summed up to obtain the basic comment score of the i-th standard comment data.

10. The method according to claim 8, characterized in that Determining the transaction score for the business product according to the basic review scores respectively corresponding to the P pieces of standard review data within the first time period includes: Determining the business feature associated with the business product in the i-th standard review data; Determining the credibility of the comment of the i-th standard comment data according to the business characteristics; Multiplying the comment credibility of the i-th standard comment data by the basic comment score to obtain a weighted comment score of the i-th standard comment data; A transaction score for the business product is determined according to the weighted review scores corresponding to the P pieces of standard review data within the first time period.

11. The method according to claim 10, characterized in that Determining the transaction score for the business product according to the weighted review scores corresponding to the P pieces of standard review data in the first time period includes: Standardizing the weighted comment score of the i-th standard comment data to obtain the standard comment score of the i-th standard comment data; The standard review scores corresponding to the P pieces of standard review data within the first time period are averaged to obtain a transaction score for the business product.

12. The method according to claim 1, characterized in that The transaction score is determined based on the review data of the business product in the first time period; the method further includes: Obtaining historical scores corresponding to Q second time periods of the business product, respectively; each historical score is a transaction score determined based on review data of the business product in the corresponding second time period, and the Q second time periods are located before the first time period; determining a change trend of the transaction sentiment of the transaction object for the business product within the first time period according to the Q second time periods, the historical scores respectively corresponding to the Q second time periods, the transaction scores, and the first time period; According to the change trend of the transaction sentiment corresponding to the first time period, score change trend information for the transaction score is generated.

13. The method according to claim 12, characterized in that The determining, according to the Q second time periods, the historical scores respectively corresponding to the Q second time periods, the transaction score and the first time period, a change trend of the transaction object's transaction sentiment for the business product within the first time period includes: Determine the decay weight of the corresponding historical score according to the time intervals between the Q second time periods and the first time period; Multiplying the historical scores corresponding to the Q second time periods by the corresponding decay weights to obtain weighted transaction scores corresponding to the Q second time periods; According to the transaction score and the weighted transaction scores respectively corresponding to the Q second time periods, a change trend of the transaction sentiment of the transaction object for the business product in the first time period is determined.

14. The method according to claim 13, characterized in that The determining, according to the transaction score and the weighted transaction scores respectively corresponding to the Q second time periods, a change trend of the transaction object's transaction sentiment for the business product within the first time period includes: The transaction score and the weighted transaction scores corresponding to the S second time periods are averaged to obtain a first average transaction score; the S second time periods are consecutively adjacent second time periods among the Q second time periods, and there is a second time period adjacent to the first time period among the S second time periods, and S is a positive integer less than Q; Averaging the transaction score and the weighted transaction scores corresponding to the Q second time periods to obtain a second average transaction score; A change trend of the transaction sentiment of the transaction object with respect to the business product during the first time period is determined according to the first average transaction score and the second average transaction score.

15. The method according to claim 14, characterized in that The determining, according to the first average transaction score and the second average transaction score, a change trend of the transaction object's transaction sentiment for the business product during the first time period includes: performing a difference processing on the first average transaction score and the second average transaction score to obtain a transaction sentiment change difference for the transaction score; The transaction sentiment change difference corresponding to the transaction score is processed with the transaction sentiment change difference of the historical score corresponding to the adjacent time period to obtain the transaction sentiment change speed for the transaction score; the adjacent time period is a second time period adjacent to the first time period among the Q second time periods; According to the transaction sentiment change difference and the transaction sentiment change speed for the transaction score, a change trend of the transaction sentiment of the transaction object for the business product within the first time period is determined.

16. The method according to claim 15, characterized in that The determining, according to the transaction sentiment change difference and the transaction sentiment change speed for the transaction score, a change trend of the transaction sentiment of the transaction object for the business product during the first time period includes: When the absolute value of the transaction sentiment change speed is greater than a first speed threshold, the transaction sentiment change difference corresponding to the transaction score is compared with the change threshold, and the transaction sentiment change speed is compared with a second speed threshold; the first speed threshold is greater than the second speed threshold; When the transaction sentiment change difference for the transaction score is greater than the change threshold, and the transaction sentiment change speed is greater than the second speed threshold, the transaction sentiment will be turned to positive, and determined as the change trend of the transaction object's transaction sentiment for the business product during the first time period; When the transaction sentiment change difference for the transaction score is less than the change threshold, and the transaction sentiment change speed is less than the second speed threshold, it will turn to negative transaction sentiment and be determined as the change trend of the transaction object's transaction sentiment for the business product during the first time period.

17. The method according to claim 14, characterized in that The method further comprises: When the change trend of the transaction object's transaction sentiment toward the business product meets the warning condition, determining a warning level for the business product according to the first average transaction score and the second average transaction score; Generating original warning content regarding the change trend of the trading sentiment according to the warning level; The warning level and the original warning content are sent to the terminal.

18. An information display device, characterized in that: The device comprises: A first display module, used to display business products and transaction scores for the business products in a product transaction application; the transaction scores are determined based on review data of the business products; A second display module, used to display a detail viewing control of the transaction score; The third display module is used to display the score change trend information for the transaction score in response to the trigger operation of the detail viewing control; the score change trend information is used to characterize the change trend of the transaction sentiment of the transaction object towards the business product, and the change trend of the transaction sentiment is determined based on the emotional feedback on the business product in the review data of the business product.

19. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 17 are implemented.

20. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 17 are implemented.

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