Fund analysis report generation method and device and electronic equipment

Abstract

The invention discloses a fund analysis report generation method and device and electronic equipment, and relates to the technical field of finance. According to the man-machine interaction method, firstly, user behavior data and user posted content are obtained, so that multi-dimensional and deep analysis of a fund discussion area is achieved, and the one-sidedness caused by the fact that a traditional analysis method only depends on basic data is avoided. And secondly, analyzing the user behavior data and the user posted content, further determining discussion area attribute data capable of representing the popularity of the discussion area and the influence condition of the user attitude on fund performance, and finally generating a fund analysis report corresponding to the discussion area attribute data, so that a fund manager intuitively masters the overall dynamic state of the fund discussion area and improves the fund analysis efficiency. According to the method, user groups and attitude tendencies thereof are identified, comprehensive and reliable data support is provided for formulating targeted operation strategies, timely coping with potential risks and optimizing fund management decisions, and the limitation that a traditional analysis method only depends on surface data is effectively broken through.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of finance, and more particularly, to a fund analysis report generation method and device and electronic equipment. BACKGROUND

[0002] In fund management, managers are faced with the dilemma of insufficient analysis means of fund discussion area. Traditional analysis methods can only provide basic data such as post quantity, like number, etc. These data can only reflect some surface phenomena and cannot deeply mine the real characteristics and behavior patterns of users. It is impossible to comprehensively understand the emotional tendency of users, so it is difficult to grasp the overall dynamics of the discussion area and market trends, which makes it lack reliable basis when formulating operation strategies, affecting the market performance and user stickiness of the fund. SUMMARY

[0003] In view of the above problems, the present application provides a fund analysis report generation method, device and electronic equipment to improve the comprehensiveness of the fund analysis report. The specific scheme is as follows:

[0004] In a first aspect, a fund analysis report generation method is provided, comprising:

[0005] Obtaining a user data set corresponding to a fund discussion area, the user data set comprising user behavior data and user post content, the user behavior data representing the interaction of users in the fund discussion area;

[0006] Determining discussion area attribute data based on the user data set, the discussion area attribute data representing the heat of the fund discussion area and the influence of user attitude on fund performance;

[0007] Generating a fund analysis report corresponding to the fund discussion area based on the discussion area attribute data.

[0008] In a possible design, in another implementation manner of the first aspect of the embodiment of the present application, the discussion area attribute data includes action optimism, which represents the optimistic tendency degree of the user's sharing behavior in the fund discussion area;

[0009] The process of determining the discussion area attribute data based on the user data set comprises:

[0010] Determining a discussion post heat value corresponding to each discussion post in the fund discussion area based on the user behavior data, and determining a user action label based on the user post content, the user action label representing the investment behavior tendency exhibited by the user in the discussion post;

[0011] Determine an action effect value based on the discussion post heat value and the user action label of each discussion post, where the action effect value represents the influence of user investment behavior on fund performance;

[0012] Determine the action optimism based on the action effect value and a fund net value change rate, where the fund net value change rate represents the degree of fund increase or decrease.

[0013] In a possible design, in another implementation manner of the first aspect of the embodiment of the present application, the discussion area attribute data includes an emotional health degree, where the emotional health degree represents the tendency of the emotional state of users in the fund discussion area;

[0014] The process of determining the discussion area attribute data based on the user data set includes:

[0015] Determine a discussion post heat value of each discussion post in the fund discussion area based on the user behavior data, determine a discussion post emotional label and a competitor identifier based on the user post content, where the discussion post emotional label represents the emotional tendency of users to the fund, and the competitor identifier represents whether there is a competitor in the discussion post;

[0016] Determine an emotional influence of the fund discussion area based on the discussion post heat value, the discussion post emotional label and the competitor identifier of each discussion post, where the emotional influence represents the influence of user emotion on fund performance;

[0017] Determine the emotional health degree based on the emotional influence and a fund net value change rate, where the fund net value change rate represents the degree of fund increase or decrease.

[0018] In a possible design, in another implementation manner of the first aspect of the embodiment of the present application, the discussion area attribute data includes a competitor cleanliness degree, where the competitor cleanliness degree represents the comprehensive proportion degree of the quantity and heat of non-competitor discussion posts in the fund discussion area, and the non-competitor discussion post represents a discussion post that does not involve competitor content;

[0019] The process of determining the discussion area attribute data based on the user data set includes:

[0020] Determine a discussion post heat value of each discussion post in the fund discussion area based on the user behavior data, determine a non-competitor discussion post in each discussion post in the fund discussion area based on the user post content;

[0021] determine a quantity comparison value and a heat comparison value based on the non-competitive discussion post, the quantity comparison value representing a ratio of a quantity of the non-competitive discussion post in the fund discussion area to a total quantity of all discussion posts, and the heat comparison value representing a ratio of a sum of discussion post heat values of the non-competitive discussion post in the fund discussion area to a sum of discussion post heat values of all discussion posts;

[0022] determine the competitive cleanliness based on the quantity comparison value and the heat comparison value.

[0023] In a possible design, in a further implementation manner of the first aspect of the embodiment of the present application, the discussion area attribute data comprises a discussion area heat value representing a heat condition of the fund discussion area in a configured time period.

[0024] The process of determining the discussion area attribute data based on the user data set comprises:

[0025] determine a discussion post heat value corresponding to each discussion post in the fund discussion area based on the user behavior data.

[0026] determine a first discussion area index score and a second discussion area index score based on the discussion post heat values and the user behavior data, the first discussion area index score representing a dimension of a post scale and a number of participants of the fund discussion area, and the second discussion area index score representing an average interaction amount of the discussion posts in the fund discussion area and a quantity of the discussion posts in different heat intervals.

[0027] perform normalization processing on the first discussion area index score and the second discussion area index score based on the full-amount fund discussion area.

[0028] perform weighted processing on the normalized first discussion area index score and the normalized second discussion area index score to obtain the discussion area heat value.

[0029] In a possible design, in a further implementation manner of the first aspect of the embodiment of the present application, the process further comprises:

[0030] generate a visual interactive interface based on the fund analysis report, the visual interactive interface comprising a data display area and a competitive comparison area, the data display area being configured to display a quantitative value corresponding to the fund analysis report, and the competitive comparison area being configured to display a comparison result of the fund discussion area and at least one competitive fund discussion area in corresponding discussion area attribute data.

[0031] In a possible design, in a further implementation manner of the first aspect of the embodiment of the present application, the process of generating the fund analysis report corresponding to the fund discussion area based on the discussion area attribute data comprises:

[0032] determining a numerical interval to which the discussion area attribute data belongs, and determining a corresponding fund analysis report according to the numerical interval to which the discussion area attribute data belongs.

[0033] In a possible design, in another implementation manner of the first aspect of the embodiment of the present application, the user data set further includes user portrait data, the discussion area attribute data includes user stickiness and core user degree, the user portrait data represents identity features and influence of a user, the user stickiness represents a participation frequency of the user in the fund discussion area, and the core user degree represents a proportion and an active degree of influential users in the fund discussion area.

[0034] The process of determining the discussion area attribute data based on the user data set includes:

[0035] The user stickiness and the core user degree are determined based on the user behavior data and the user portrait data.

[0036] In a second aspect, a fund analysis report generation apparatus is provided, which includes:

[0037] a user data obtaining unit configured to obtain a user data set corresponding to a fund discussion area, the user data set including user behavior data and user post content, and the user behavior data representing an interaction of a user in the fund discussion area;

[0038] an attribute data generation unit configured to determine discussion area attribute data based on the user data set, the discussion area attribute data representing a heat of the fund discussion area and an influence of user attitudes on fund performance;

[0039] a report generation unit configured to generate a fund analysis report corresponding to the fund discussion area based on the discussion area attribute data.

[0040] In a third aspect, an electronic device is provided, which includes a memory and a processor.

[0041] The memory is configured to store a program.

[0042] The processor is configured to execute the program, so as to implement each step of the fund analysis report generation method described in any one of the preceding first aspects of the present application.

[0043] By the technical scheme, firstly, the user behavior data and the user post content are acquired, multi-dimensional and deep analysis of the fund discussion area is realized, and one-sidedness caused by traditional analysis methods which only rely on basic data is avoided. Secondly, the user behavior data and the user post content are analyzed, and then discussion area attribute data capable of representing the discussion area heat and the influence of the user attitude on the fund performance is determined, finally, the fund analysis report corresponding to the generated discussion area attribute data is generated, so that the fund manager can intuitively master the overall dynamics of the fund discussion area and identify the user group and the attitude tendency, and the fund manager can provide comprehensive and reliable data support for formulating targeted operation strategy, responding to potential risks in time and optimizing fund management decision, and the limitation of traditional analysis methods which only rely on surface data is effectively broken through. BRIEF DESCRIPTION OF DRAWINGS

[0044] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments with reference made to the accompanying drawings. The drawings are for purposes of illustration only and are not intended to be limiting in any respect. Moreover, the use of the same reference symbols in different drawings indicates similar or identical items.

[0045] Figure 1 A flowchart of a fund analysis report generation method provided by the present application;

[0046] Figure 2 A data display area schematic diagram provided by an embodiment of the present application;

[0047] Figure 3 A structure schematic diagram of a fund analysis report generation device provided by an embodiment of the present application;

[0048] Figure 4 A structure schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0049] Before introducing the scheme of the present application, first, some technical terms involved in the embodiments of the present application are explained and described:

[0050] Fund: an investment tool, which collects funds of numerous investors, forms independent assets, is managed by a fund custodian, is managed by a fund manager, and realizes asset value-added by investing in stocks, bonds, money market tools and other financial assets.

[0051] The technical scheme in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0052] The present application can be applied in the field of financial technology, and plays an important role in fund analysis and management. In the traditional fund analysis process, there are many deficiencies in the analysis of fund discussion areas, such as staying in single-dimensional surface data statistics, such as only focusing on the number of posts, reading numbers, etc., lacking comprehensive consideration of the depth and breadth of fund analysis, lacking comprehensive consideration of multi-dimensional factors such as public opinion in fund discussion areas, and being difficult to comprehensively and dynamically reflect the real situation and potential risks of funds. In addition, the existing analysis method is time-consuming and laborious when generating reports, and it is difficult to meet the large-scale and high-frequency analysis demand.

[0053] In order to realize more efficient, accurate and comprehensive fund analysis report generation, the present application integrates multi-dimensional indicators, performs in-depth analysis and calculation, and automatically generates professional and easy-to-understand analysis reports through a fund analysis report generation method. Not only improves the comprehensiveness and accuracy of the analysis, but also greatly improves the automation and efficiency of the report generation, provides more powerful data support and decision basis for fund institutions or fund managers to formulate operation strategies and understand investor sentiment dynamics, and promotes the development of fund analysis towards intelligent and professional direction.

[0054] The present application provides a fund analysis report generation method, and the present application scheme can be realized based on a terminal with data processing capability, which can be a mobile phone, a computer, etc.

[0055] Next, refer to Figure 1 , Figure 1 The present application provides a flowchart of a fund analysis report generation method, and the fund analysis report generation method of the present application can be realized through a fund analysis report generation system deployed on a terminal, as follows, specifically including the following steps:

[0056] Step S100, acquiring a user data set corresponding to a fund discussion area.

[0057] Specifically, a user data set corresponding to the fund discussion area is acquired, which can include user behavior data and user post content. The user behavior data can reflect the user's interaction in the fund discussion area, such as reading behavior, liking behavior, and following behavior. The user behavior data can intuitively reflect the user's attention, recognition, and active participation in the discussion, and is the basis for measuring the discussion area heat, user stickiness, and other important indicators. At the same time, the user post content can include the user's various views on the fund, emotional expression, investment experience sharing, and single behavior, etc. Through deep mining and analysis of these contents, the user's emotional tendency, action intention, and attitude towards competitors can be further understood, providing a data basis for multi-dimensional index calculation. In an optional manner, in order to realize this process, data collection technology and natural language processing technology can be used to track the user's operation behavior in the discussion area in real time, accurately record the interaction track, and efficiently extract and preprocess the user post content, ensuring that the acquired data is comprehensive and accurate, and laying a solid data foundation for further comprehensive analysis and evaluation of the fund discussion area.

[0058] Step S110, determining discussion area attribute data based on the user data set.

[0059] Specifically, the discussion area attribute data of the fund discussion area is determined based on the user data set, that is, through comprehensive consideration of the user behavior data and the post content, the discussion area attribute data of the fund discussion area is determined. The discussion area attribute data represents the heat of the fund discussion area and the influence of the user attitude on the performance of the fund. The discussion area attribute data comprehensively describes the comprehensive state of the fund discussion area, providing multi-dimensional and in-depth data insight for the fund institution.

[0060] Step S120, generating a fund analysis report corresponding to the fund discussion area based on the discussion area attribute data.

[0061] Specifically, in the fund analysis report, the discussion area attribute data is integrated and analyzed, which can be presented in the form of language and chart, forming a comprehensive and in-depth fund analysis report. Not only does it provide multi-dimensional data support for fund managers to help them grasp the overall situation of the fund discussion area, but also provides a strong basis for them to develop operation strategies and improve user experience.

[0062] The embodiment first realizes multi-dimensional and deep analysis of the fund discussion area by obtaining user behavior data and user post content, avoiding the one-sidedness caused by the traditional analysis method which only relies on basic data. Secondly, the user behavior data and the user post content are analyzed to determine the discussion area attribute data which can represent the discussion area heat and the influence of user attitude on fund performance. The final fund analysis report corresponding to the generated discussion area attribute data enables fund managers to intuitively master the overall dynamics of the fund discussion area and identify user groups and their attitude tendency, providing comprehensive and reliable data support for formulating targeted operation strategies, timely responding to potential risks and optimizing fund management decisions, effectively breaking through the limitations of traditional analysis methods which only rely on surface data.

[0063] Further, in some embodiments of the present application, the discussion area attribute data can include any one or more of action optimism, emotional health, discussion area heat value, competitor cleanliness, user stickiness, and core user degree, and the discussion area attribute data can be determined by a user data set. The following will introduce this part in detail.

[0064] In an optional manner, the discussion area attribute data can include action optimism, which represents the optimistic tendency degree of the user's sharing behavior in the fund discussion area. In the process of determining the action optimism, the discussion post heat value corresponding to each discussion post in the fund discussion area can be determined according to the user behavior data. The discussion post heat value corresponding to each discussion post can be calculated based on the reading number, the number of likes and the number of follow-up posts of a single post and the following formula:

[0065] .

[0066] The discussion post heat value reflects the popularity and influence of the discussion post in the discussion area.

[0067] Then, the user post content can be analyzed in depth to identify and determine the user action label. The user action label represents the investment behavior tendency of the user in the discussion post, such as whether it contains sharing behavior and the specific nature of the sharing (profit display or operation sharing) and the like.

[0068] Based on the discussion post heat value of each discussion post and the user action label, the action effect value is further calculated, which is used to represent the influence of the user's investment behavior on the fund performance. The specific formula is:

[0069] ;

[0070] Wherein, the user action label score is obtained according to the analysis of the system on the user post content, and the positive and negative values respectively represent optimistic or pessimistic investment behavior tendency. The higher the heat of the post, the more significant the action effect.

[0071] Finally, the action optimism is determined by combining the action effect value and the fund net value change rate, and the fund net value change rate represents the degree of fund price increase or decrease. The specific formula can be as follows:

[0072] 。

[0073] The action optimism not only reflects the user's behavior tendency of sharing, but also reveals the potential influence of the behavior tendency on the fund performance in a specific market environment, providing a more accurate and comprehensive analysis perspective for the fund institution to deeply understand the complex relationship between investor behavior and fund performance.

[0074] An alternative way, the discussion area attribute data can include emotional health, which represents the tendency of user's emotional state in the fund discussion area, providing an important basis for evaluating the influence of user emotion on fund performance. In the process of determining the emotional health, first, according to the user behavior data such as the number of reads, likes and follow-up posts of a single post, the discussion post heat value of each discussion post is calculated by substituting the following formula:

[0075] 。

[0076] Then the user's post content can be analyzed to identify and determine the discussion post emotion label and competitor identification. The discussion post emotion label can be determined by the AI large model to judge the emotional tendency of the post content, and the discussion post emotion label represents the user's emotional tendency (positive or negative) to the fund, and the competitor identification is used to judge whether the competitor is mentioned in the discussion post.

[0077] Based on the heat value, emotion label and competitor identification of each discussion post, the emotional influence is further calculated, which comprehensively considers the post heat, emotional tendency and whether it involves competitors, and is used to measure the influence degree of user emotion on fund performance. The specific formula is as follows:

[0078] ;

[0079] Wherein, the competitor identification score is 1 if the discussion post contains the competitor, otherwise it is 0; the emotion label score is positive if it is positive emotion, and negative if it is negative emotion.

[0080] Finally, the emotional health is determined by combining the emotional influence and the fund net value change rate. The specific formula is as follows:

[0081] 。

[0082] The emotional health degree not only reflects the emotional state tendency of the user in the discussion area, but also reveals the potential influence of the emotional state on the fund performance in a specific market environment, providing a more accurate and comprehensive analysis perspective for the fund institution to deeply understand the complex relationship between investor emotion and fund performance.

[0083] In an optional manner, the discussion area attribute data can include a competitor cleanliness. The competitor cleanliness represents the comprehensive proportion of non-competitor discussion posts in the fund discussion area in terms of quantity and heat. The non-competitor discussion post represents a discussion post that does not involve competitor content. To determine the competitor cleanliness, the discussion post heat value of the discussion post can be calculated based on the above manner, which will not be described here. Then, the non-competitor discussion post is identified based on the user post content in each discussion post in the fund discussion area. The non-competitor discussion post does not contain any competitor-related information. The quantity comparison value can be calculated by counting the number of non-competitor discussion posts and the total number of posts.

[0084] .

[0085] Meanwhile, the discussion post heat values of all discussion posts are summarized, the sum of the discussion post heat values of the non-competitor discussion posts is calculated, and then divided by the sum of the discussion post heat values of all discussion posts to obtain the heat comparison value.

[0086] .

[0087] Finally, the competitor cleanliness is determined by combining the quantity comparison value and the heat comparison value.

[0088] .

[0089] The competitor cleanliness can reflect the comprehensive proportion of the non-competitor discussion post in the discussion area. The closer the value is to 1, the higher the contribution of the non-competitor discussion post in terms of quantity and heat, and the smaller the influence of the competitor on the discussion area. This helps the fund institution to more accurately identify and evaluate the influence of the competitor in the discussion area, so as to develop more effective operation strategies.

[0090] In an optional manner, the discussion area attribute data can include a discussion area heat value. The discussion area heat value represents the heat of the fund discussion area in the configured time period. To determine the discussion area heat value, first, the discussion post heat value of each discussion post in the fund discussion area is calculated based on the user behavior data.

[0091] Then, the discussion post heat value and the user behavior data are used to determine two first-level indicators: a first discussion area indicator score and a second discussion area indicator score. The first discussion area indicator score reflects the user post size and the number of participants in the fund discussion area, and the calculation formula is:

[0092] .

[0093] The second discussion area index score focuses on the average interaction amount of discussion posts in the discussion area and the number of discussion posts in different heat intervals, and the calculation formula is:

[0094] .

[0095] After determining the scores of the above two first-level indicators, the first discussion area index score and the second discussion area index score are normalized based on the full-fund discussion area. Finally, according to the weight distribution principle, the normalized first discussion area index score and the normalized second discussion area index score are weighted to obtain the final discussion area heat value:

[0096] .

[0097] The discussion area heat value can comprehensively reflect the overall heat of the fund discussion area, providing an important basis for fund analysis.

[0098] An optional way, the user data set also includes user portrait data, which represents the identity characteristics and influence of the user. The discussion area attribute data further covers user stickiness and core user degree. Among them, the user stickiness represents the participation frequency of the user in the fund discussion area. Specifically, it reflects the user's continuous attention and participation degree in a certain fund discussion area within a period of time, for example, the frequency of the user posting or replying multiple times within a week. The core user degree represents the proportion and activity level of influential users in the fund discussion area. Core users can include V-verified users, users holding the fund, and users whose views and content can trigger widespread attention and discussion in the discussion area.

[0099] First, the user stickiness is calculated by analyzing user behavior data and user portrait data. Specifically, the calculation of user stickiness may involve statistical behavior data such as the number of posts, reply frequency and access duration of the user within a certain time period, and combined with the identity characteristics in the user portrait data, such as distinguishing between ordinary users and experienced investors, to more accurately assess the user's participation frequency and stickiness degree.

[0100] At the same time, the determination of core user degree also needs to consider user behavior data and user portrait data. On the one hand, by analyzing the post quantity, content quality and interaction of V-verified users and fund holders, on the other hand, combined with the influence indicators in the user portrait data, such as the number of fans, the spread range of past content, etc., to quantify the proportion and activity level of core users in the discussion area.

[0101] An optional way, the calculation formula of user stickiness can be:

[0102] .

[0103] An optional way, the formula of core user degree can be:

[0104] .

[0105] User stickiness and core user degree can provide deeper user group insight for fund discussion area, help fund institutions better understand user behavior patterns, identify and cultivate core user groups, so as to formulate more accurate operation strategy, improve the activity of discussion area and user value.

[0106] Further, in some embodiments of the present application, the process of generating the fund analysis report corresponding to the fund discussion area based on the discussion area attribute data can be further introduced, which will be introduced in detail below.

[0107] First of all, it is necessary to determine the numerical interval to which the discussion area attribute data belongs. The value range of the discussion area attribute data can be clearly divided. An optional way is to divide the discussion area heat value into three intervals of high, medium and low, and the emotion health degree may be divided into intervals such as positive, neutral and negative.

[0108] Then, according to the numerical interval to which the discussion area attribute data belongs, the corresponding fund analysis report is determined. An optional way is to establish a mapping mechanism to match each numerical interval with the corresponding analysis conclusion and suggestion. For example, if the heat value of the discussion area falls in the high interval, the analysis report may emphasize the high attention of the fund among investors, and suggest further analysis of the reason; If the emotion health degree is positive, the report may point out the positive emotion of the investors to the fund and explore its potential positive impact on the fund performance. In actual operation, data analysis software and algorithm model can be used to realize it, so as to ensure the accuracy and efficiency of the analysis. In this way, the fund analysis report can provide valuable insight and decision support for fund managers, helping them better understand and respond to market dynamics.

[0109] An optional way is to determine the emotion health degree numerical interval to which the emotion health degree belongs, to determine the corresponding first fund analysis result according to the emotion health degree numerical interval to which the emotion health degree belongs, the first fund analysis result represents the influence of emotion health degree on fund performance.

[0110] The numerical interval of emotion health degree is divided as follows:

[0111] High emotional health interval: when the emotional health is greater than or equal to 0.8, it indicates that the overall emotion of the discussion area is relatively positive and optimistic, and the positive evaluation of the fund by the investors dominates, which can represent that the fund has good recognition and investor confidence in the market, and may have a positive impact on the short-term and long-term performance of the fund.

[0112] Medium emotional health interval: when the emotional health is between 0.4 and 0.8, it indicates that the emotion of the discussion area is relatively neutral, and the opinions of the investors are balanced, with both positive and negative evaluations, which can represent that the fund performance is relatively stable, and the market attention and evaluation of the fund are at a medium level, and the impact on the fund performance is relatively small.

[0113] Low emotional health interval: when the emotional health is less than 0.4, it indicates that the overall emotion of the discussion area is relatively negative, and there are more negative evaluations of the fund by the investors, which can represent that the fund is facing some challenges or problems in the market, and needs to attract the attention of the fund managers.

[0114] An optional way is to determine the action optimism value interval to which the action optimism belongs, determine the corresponding second fund analysis result based on the action optimism value interval, and the second fund analysis result represents the influence of the action optimism on the fund performance.

[0115] The value interval of the action optimism is divided as follows:

[0116] High action optimism interval: when the action optimism is greater than or equal to 0.7, it indicates that the discussion area user's single behavior presents a high degree of optimistic tendency, and the investment behavior of the investors is relatively positive, which can represent that the fund performs well in the market, and the investors have confidence in its future performance, which may attract more investor attention and participation.

[0117] Medium action optimism interval: when the action optimism is between 0.3 and 0.7, it indicates that the optimistic tendency of the discussion area user's single behavior is relatively general, and the investment behavior of the investors is relatively cautious, which can represent that the fund performance is relatively stable, and the market attention and investment willingness to the fund are at a medium level.

[0118] Low action optimism interval: when the action optimism is less than 0.3, it indicates that the discussion area user's single behavior presents a low degree of optimistic tendency, and there may be more negative investment behavior, which can represent that the fund is facing certain pressure in the market, and the investors have a relatively conservative attitude towards its future performance.

[0119] An optional way is to determine the competitor cleanliness value interval to which the competitor cleanliness belongs, determine the corresponding third fund analysis result based on the competitor cleanliness value interval, and the third fund analysis result represents the influence of the competitor cleanliness on the fund performance; the value interval of the competitor cleanliness is divided as follows:

[0120] High competitor cleanliness interval: when the competitor cleanliness is greater than or equal to 0.85, it means that the non-competitor discussion posts in the discussion area dominate in terms of quantity and heat, and the competitor has less influence on the discussion area, which can represent that the fund has strong independence and competitiveness in the market and can attract more investor attention and discussion.

[0121] Medium competitor cleanliness interval: when the competitor cleanliness is between 0.6 and 0.85, it means that the number and heat of non-competitor discussion posts and competitor discussion posts in the discussion area are relatively balanced, and the competitor has some influence on the discussion area, which can represent that the fund faces certain competitive pressure in the market, but still has great development space.

[0122] Low competitor cleanliness interval: when the competitor cleanliness is less than 0.6, it means that the competitor discussion posts in the discussion area occupy a large proportion in terms of quantity and heat, and the competitor has a greater influence on the discussion area, which can represent that the fund faces greater competitive challenges in the market and needs to further optimize its performance and improve competitiveness.

[0123] Similarly, based on the above method, the fourth fund analysis result corresponding to the discussion area heat value is obtained.

[0124] An optional way is to determine the user stickiness value interval to which the user stickiness belongs, and determine the corresponding fourth fund analysis result according to the user stickiness value interval, and the fourth fund analysis result represents the influence of the user stickiness on the fund performance:

[0125] First, the total fund discussion area is grouped according to the heat value, and every 50 discussion areas are a group (such as the first 1 to 50 are the first group, the 51 to 100 are the second group, and so on). If there are less than 50 discussion areas left at the end, they will be merged into the last group. If the heat values are the same, sort them by fund name to ensure the uniqueness and rationality of the grouping. Then calculate the average user stickiness value of each group of discussion areas. The specific method can be to add the normalized user stickiness values of all discussion areas in the group and divide by the number of discussion areas in the group to get the average value of the group.

[0126] User stickiness value interval and analysis result correspondence:

[0127] High user stickiness interval: when the user stickiness is greater than a certain proportion of the average value, it means that the discussion area users have a high participation frequency and stickiness, and the investors have a high degree of attention and loyalty to the fund, which can represent that the fund can attract and retain investors and form a stable investor group, which has a positive impact on the long-term performance and size growth of the fund.

[0128] Medium user stickiness interval: When the user stickiness is between the average value and a certain percentage of the average value, it indicates that the participation frequency and stickiness of the discussion area users are at a medium level, and the attention of investors to the fund is general, which can represent that the attractiveness and retention ability of the fund among investors need to be improved, and the user experience and operation strategy need to be further optimized.

[0129] Low user stickiness interval: When the user stickiness is less than a certain percentage of the average value, it indicates that the participation frequency and stickiness of the discussion area users are low, and the attention and loyalty of investors to the fund are insufficient, which can represent that the fund has great challenges in attracting and retaining investors, and effective measures need to be taken to improve.

[0130] An optional way is to determine the core user degree value interval to which the core user degree belongs, and determine the corresponding fifth fund analysis result based on the core user degree value interval. The fifth fund analysis result represents the influence of the core user degree on the fund performance. Similarly, the full-fund discussion area is grouped according to the discussion area heat value, and the average core user value is calculated in each group. The method is the same as the user stickiness described above.

[0131] Numerical interval and analysis result correspondence:

[0132] High core user degree interval: When the core user degree is greater than a certain percentage of the average value, it indicates that the proportion and activity level of influential users in the discussion area are high. These core users usually include V-verified users and fund holders, and their active participation can drive the discussion enthusiasm of other users, improve the overall quality and influence of the discussion area, and have a significant positive effect on the market performance and brand image of the fund.

[0133] Medium core user degree interval: When the core user degree is between the average value and a certain percentage of the average value, it indicates that the participation and influence of core users in the discussion area are at a medium level. Although core users have a certain driving effect on the discussion area, the overall influence is limited. Fund institutions can further improve the activity and influence of the discussion area by tapping and cultivating potential core users.

[0134] Low core user degree interval: When the core user degree is less than a certain percentage of the average value, it indicates that the participation and influence of core users in the discussion area are low, and there is a lack of users with strong influence and appeal, which may lead to low activity and content quality of the discussion area, making it difficult to attract more investor attention. Fund institutions need to take effective measures to attract and cultivate core users and improve the competitiveness and attractiveness of the discussion area.

[0135] Based on the analysis results of the respective funds, a fund analysis report containing any one or more of the fund results is generated, which can provide fund institutions with comprehensive market insights and investor behavior analysis, help them accurately grasp market dynamics and investor demand, and make more scientific and reasonable operation strategies and investment decisions to improve the market competitiveness and investor satisfaction of the funds.

[0136] An optional way is to generate a visual interactive interface based on the fund public opinion report. The visual interactive interface can include a data display area, a time period selection control, a competitor comparison area, and an index filtering control. Referring to Figure 2 , Figure 2 A data display area provided by an embodiment of the present application is shown in a schematic diagram. The data display area is used to intuitively present the quantitative values and tendency identifiers corresponding to the fund analysis report, and can also display the specific values and trends of the attribute data of each discussion area in the form of charts, graphs, etc., to help users quickly understand the indicators such as the heat, emotional health, action optimism, and competitor cleanliness of the fund discussion area.

[0137] The time period selection control allows the user to select a specific time period, such as the past week, the past month, the past three months, etc., to view the corresponding fund analysis report in that time period, meet the user's attention needs for different time period data, and facilitate observation of the dynamic changes and long-term trends of the fund discussion area.

[0138] The competitor comparison area is used to display the comparison results of the fund discussion area and at least one competitor fund discussion area in the corresponding discussion area attribute data, which can be clearly presented in the form of bar charts, line charts, etc., to clearly show the differences and relative positions of the fund and the competitor fund in each indicator, and provide intuitive basis for the user to evaluate the market competitiveness of the fund.

[0139] The index filtering control supports the user to select at least one fund analysis report corresponding to the discussion area attribute data individually or in combination. The user can flexibly select the indicator combination of interest according to their own needs, such as viewing only the emotional health and action optimism, or viewing the comprehensive analysis report of all attribute data at the same time, to achieve a personalized data analysis experience.

[0140] The above describes a fund analysis report generation method provided by an embodiment of the present application. The following will introduce a device for executing the fund analysis report generation method described above.

[0141] Please refer to Figure 3 , Figure 3 A structural schematic diagram of a fund analysis report generation device provided by an embodiment of the present application is shown in Figure 3 As shown in the figure, the fund analysis report generation device includes:

[0142] The user data acquisition unit 11 is configured to acquire a user data set corresponding to the fund discussion area, the user data set including user behavior data and user post content, and the user behavior data representing user interaction in the fund discussion area;

[0143] The attribute data generation unit 12 is configured to determine discussion area attribute data based on the user data set, the discussion area attribute data representing the heat of the fund discussion area and the influence of user attitude on fund performance;

[0144] The report generation unit 13 is configured to generate a fund analysis report corresponding to the fund discussion area based on the discussion area attribute data.

[0145] In a possible implementation, the discussion area attribute data includes action optimism, which represents the optimistic tendency degree of the user's sharing behavior in the fund discussion area. The process of determining the discussion area attribute data based on the user data set by the attribute data generation unit 12 includes:

[0146] determining a discussion post heat value corresponding to each discussion post in the fund discussion area based on the user behavior data, and determining a user action label based on the user post content, the user action label representing the investment behavior tendency exhibited by the user in the discussion post;

[0147] determining an action effect value based on the discussion post heat value and the user action label corresponding to each discussion post, the action effect value representing the influence of the user's investment behavior on fund performance;

[0148] determining the action optimism based on the action effect value and a fund net value change rate, the fund net value change rate representing the degree of fund appreciation or depreciation.

[0149] In a possible implementation, the discussion area attribute data includes emotional health, which represents the tendency of the emotional state of the user in the fund discussion area. The process of determining the discussion area attribute data based on the user data set by the attribute data generation unit 12 includes:

[0150] determining a discussion post heat value corresponding to each discussion post in the fund discussion area based on the user behavior data, and determining a discussion post emotion label and a competitor identifier based on the user post content, the discussion post emotion label representing the emotional tendency of the user to the fund, and the competitor identifier representing whether there is a competitor in the discussion post;

[0151] determining an emotional influence of the fund discussion area based on the discussion post heat value, the discussion post emotion label, and the competitor identifier corresponding to each discussion post, the emotional influence representing the influence of the user's emotion on fund performance;

[0152] Determine the emotional health degree based on the emotional influence and the fund net value change rate, and the fund net value change rate represents the degree of fund appreciation or depreciation.

[0153] In a possible implementation, the discussion area attribute data includes a competitor cleanliness, the competitor cleanliness represents a comprehensive proportion of non-competitor discussion posts in the fund discussion area in terms of quantity and heat, the non-competitor discussion post represents a discussion post not involving competitor content, and the process of determining the discussion area attribute data by the attribute data generation unit 12 based on the user data set includes:

[0154] Determine the discussion post heat value corresponding to each discussion post in the fund discussion area based on the user behavior data, and determine the non-competitor discussion post based on the user post content in each discussion post in the fund discussion area.

[0155] Determine the quantity comparison value and the heat comparison value based on the non-competitor discussion post, the quantity comparison value represents the ratio of the number of non-competitor discussion posts in the fund discussion area to the number of all discussion posts, and the heat comparison value represents the ratio of the sum of the discussion post heat values of the non-competitor discussion posts in the fund discussion area to the sum of the discussion post heat values of all discussion posts.

[0156] Determine the competitor cleanliness based on the quantity comparison value and the heat comparison value.

[0157] In a possible implementation, the discussion area attribute data includes a discussion area heat value, the discussion area heat value represents the heat condition of the fund discussion area in a configured time period, and the process of determining the discussion area attribute data by the attribute data generation unit 12 based on the user data set includes:

[0158] Determine the discussion post heat value corresponding to each discussion post in the fund discussion area based on the user behavior data.

[0159] Determine a first discussion area index score and a second discussion area index score based on the discussion post heat value and the user behavior data, the first discussion area index score represents the user post scale and the number of participants in the fund discussion area, and the second discussion area index score represents the average interaction amount of the discussion post and the number of discussion posts in different heat intervals in the fund discussion area.

[0160] Perform normalization processing on the first discussion area index score and the second discussion area index score based on the full-amount fund discussion area.

[0161] Obtain the discussion area heat value by performing weighted processing on the normalized first discussion area index score and the second discussion area index score.

[0162] In a possible implementation, the fund analysis report generation device of the embodiment of the application further comprises:

[0163] The interaction interface determining unit is configured to determine a visual interaction interface based on the fund analysis report, the visual interaction interface including a data display area and a competitor comparison area, the data display area being configured to display quantitative values corresponding to the fund analysis report, and the competitor comparison area being configured to display comparison results of the fund discussion area and at least one competitor fund discussion area in terms of corresponding discussion area attribute data.

[0164] In a possible implementation, the process of generating, by the report generating unit 13, the fund analysis report corresponding to the fund discussion area based on the discussion area attribute data includes:

[0165] determining a numerical interval to which the discussion area attribute data belongs, and determining the corresponding fund analysis report according to the numerical interval to which the discussion area attribute data belongs.

[0166] In a possible implementation, the user data set further includes user portrait data, the discussion area attribute data includes user stickiness and core user degree, the user portrait data representing identity features and influence of a user, the user stickiness representing a participation frequency of the user in the fund discussion area, and the core user degree representing a proportion and activity level of influential users in the fund discussion area, and the process of determining, by the attribute data generating unit 12, the discussion area attribute data based on the user data set includes:

[0167] determining the user stickiness and the core user degree based on the user behavior data and the user portrait data.

[0168] The embodiments of the present application further provide an electronic device. Referring to FIG. 1, a structural schematic diagram of an electronic device suitable for implementing the electronic device in the embodiments of the present application is shown. The electronic device in the embodiments of the present application can include, but is not limited to, fixed terminals such as mobile phones, notebook computers, PDAs (Personal Digital Assistants), PADs (Tablet Personal Computers), desktop computers, and the like. Figure 4 The electronic device shown in FIG. 1 is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present application. Figure 4 The electronic device shown in FIG. 1 is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present application.

[0169] As shown in FIG. 1, the electronic device can include a processor 11, a memory 12, a communication interface 13, and a power supply 14. Figure 4As shown, the electronic device can include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601 that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 602 or loaded into a random access memory (RAM) 603 from a storage device 608. In a state in which the electronic device is powered on, various programs and data required for operation of the electronic device are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0170] In general, the following devices can be connected to the I / O interface 605: input devices 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 608 including, for example, a memory card, a hard disk, etc.; and communication devices 609. The communication devices 609 can allow the electronic device to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 An electronic device having various devices is shown, but it is understood that all of the shown devices are not required to be implemented or present. More or less devices can alternatively be implemented or present.

[0171] It should be further noted that the above-described device embodiments are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment. In addition, the connection relationship between the modules in the device embodiment provided in the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines.

[0172] Those skilled in the art can clearly understand that the application can be implemented by means of software plus necessary universal hardware, and of course can also be implemented by means of dedicated hardware including special integrated circuit, special CPU, special memory, special component, etc. Generally, functions completed by computer program can be easily implemented by corresponding hardware, and specific hardware structure for implementing the same function can be various, such as analog circuit, digital circuit or special circuit, etc. However, for the application, software program implementation is a better embodiment. Based on such understanding, the technical solution of the application can be embodied in the form of software product, which is stored in a readable storage medium, such as computer floppy disk, U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc., and includes a plurality of instructions for making a computer device (which can be a personal computer, training device or network device, etc.) execute the method described in various embodiments of the application.

[0173] In the above embodiments, all or part can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part can be implemented in the form of a computer program product.

[0174] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, training device or data center to another website, computer, training device or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be stored by the computer or the data storage device such as training device, data center, etc. integrated with one or more available media. The available media can be magnetic media (such as floppy disk, hard disk, magnetic tape), optical media (such as DVD) or semiconductor media (such as solid state disk (SSD)) and the like.

[0175] Although the embodiments of the application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the application.

[0176] The various embodiments described in this specification are presented by way of example, and each embodiment is not necessarily composed of all features described with respect to other embodiments. It is contemplated that a person of ordinary skill in the art will be able to practice the application with the described embodiments, but with fewer than all of the described features, and that a person of ordinary skill in the art will be able to practice the application with more than all of the described features, and with other structures, materials, and / or components. Therefore, these claims are not to be limited to the specific embodiments described herein, but rather are to

Claims

1. A method for generating a fund analysis report, characterized in that: include: Obtaining a user data set corresponding to the fund discussion area, wherein the user data set includes user behavior data and user posting content, wherein the user behavior data represents user interaction in the fund discussion area; Determining discussion forum attribute data based on the user data set, wherein the discussion forum attribute data represents the popularity of the fund discussion forum and the impact of user attitudes on fund performance; A fund analysis report corresponding to the fund discussion forum is generated based on the discussion forum attribute data.

2. The method according to claim 1, characterized in that The forum attribute data includes action optimism, which represents the degree of optimism shown by the users' order-sharing behavior in the fund discussion forum; The process of determining the discussion forum attribute data based on the user data set includes: Determining the discussion post popularity value corresponding to each discussion post in the fund discussion area based on the user behavior data, and determining the user action tag based on the content of the user post, wherein the user action tag represents the investment behavior tendency displayed by the user in the discussion post; Determining an action effect value based on the discussion post heat value and the user action tag corresponding to each discussion post, wherein the action effect value is used to represent the impact of the user's investment behavior on the fund performance; The action optimism is determined based on the action effect value and the fund net value change rate, and the fund net value change rate represents the degree of increase or decrease of the fund.

3. The method according to claim 1, characterized in that The discussion forum attribute data includes emotional health, which represents the tendency of the emotional state of users in the fund discussion forum; The process of determining forum attribute data based on the user data set includes: Determine the discussion post popularity value corresponding to each discussion post in the fund discussion area based on the user behavior data, and determine the discussion post sentiment label and competitor product identifier based on the content of the user post, wherein the discussion post sentiment label represents the user's emotional inclination towards the fund, and the competitor product identifier represents whether there is a competitor product in the discussion post; Determining the emotional influence of the fund discussion area based on the discussion post heat value, the discussion post emotional tag, and the competitor identifier corresponding to each discussion post, where the emotional influence represents the impact of user emotions on fund performance; The emotional health is determined based on the emotional influence and the fund net value change rate, where the fund net value change rate represents the degree of increase or decrease in the fund.

4. The method according to claim 1, wherein The discussion forum attribute data includes competitor cleanliness, which represents the comprehensive proportion of non-competitive discussion posts in terms of quantity and popularity in the fund discussion forum. The non-competitive discussion posts represent discussion posts that do not involve competitive content. The process of determining forum attribute data based on the user data set includes: Determine the discussion post popularity value corresponding to each discussion post in the fund discussion area based on the user behavior data, and determine non-competing discussion posts in each discussion post in the fund discussion area based on the content of the user post; Determining a quantity comparison value and a heat comparison value based on the non-competing discussion posts, the quantity comparison value representing the ratio of the number of non-competing discussion posts in the fund discussion area to the number of all discussion posts, and the heat comparison value representing the ratio of the sum of the discussion post heat values ​​of the non-competing discussion posts in the fund discussion area to the sum of the discussion post heat values ​​of all discussion posts; The cleanliness of the competing product is determined based on the quantity comparison value and the heat comparison value.

5. The method according to claim 1, wherein The discussion forum attribute data includes a discussion forum popularity value, which represents the popularity of the fund discussion forum within a configured time period; The process of determining forum attribute data based on the user data set includes: Determine the discussion post popularity value corresponding to each discussion post in the fund discussion area based on the user behavior data; Determining a first discussion area index score and a second discussion area index score based on the popularity value of each discussion post and the user behavior data, wherein the first discussion area index score represents the scale of user posts and the number of participants in the fund discussion area, and the second discussion area index score represents the average interaction volume of discussion posts in the fund discussion area and the number of discussion posts in different popularity ranges; Normalizing the first discussion area indicator scores and the second discussion area indicator scores based on the entire fund discussion area; The normalized first discussion area index score and the second discussion area index score are weighted to obtain the discussion area heat value.

6. The method according to claim 1, wherein Also includes: A visual interactive interface is generated based on the fund analysis report, and the visual interactive interface includes a data display area and a competitive product comparison area. The data display area is used to display the quantitative values ​​corresponding to the fund analysis report, and the competitive product comparison area is used to display the comparison results between the fund discussion area and at least one competitive fund discussion area in the corresponding discussion area attribute data.

7. The method according to claim 1, characterized in that The process of generating a fund analysis report corresponding to the fund discussion forum based on the discussion forum attribute data includes: Determine the numerical range to which the discussion forum attribute data belongs, and determine the corresponding fund analysis report according to the numerical range.

8. The method according to claim 1, characterized in that The user data set also includes user portrait data, and the discussion forum attribute data includes user stickiness and core user degree. The user portrait data represents the identity characteristics and influence of the user, the user stickiness represents the frequency of user participation in the fund discussion forum, and the core user degree represents the proportion and activity level of influential users in the fund discussion forum. The process of determining forum attribute data based on the user data set includes: The user stickiness and the core user degree are determined based on the user behavior data and the user portrait data.

9. A fund analysis report generating device, characterized in that: include: A user data acquisition unit, configured to acquire a user data set corresponding to the fund discussion area, wherein the user data set includes user behavior data and user posting content, wherein the user behavior data represents the user's interaction in the fund discussion area; an attribute data generating unit, configured to determine discussion forum attribute data based on the user data set, wherein the discussion forum attribute data represents the popularity of the fund discussion forum and the impact of user attitudes on fund performance; A report generating unit is used to generate a fund analysis report corresponding to the fund discussion forum based on the discussion forum attribute data.

10. An electronic device, characterized in that: include: memory and processor; The memory is used to store programs; The processor is configured to execute the program to implement each step of the fund analysis report generation method according to any one of claims 1 to 8.

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