New media data online analysis method and device

Abstract

The invention discloses a new media data online analysis method and device, and a server side method of a BI system comprises the steps: collecting user interaction data about a target product from a plurality of new media platforms at the same time through a preset distributed data collection architecture; extracting emotional description information capable of reflecting the emotional tendency of the user to the target product from the user interaction data of each new media platform to obtain multi-source emotional description information; inputting the multi-source emotion description information into a preset new media data online analysis component, and outputting a user emotion fusion feature corresponding to the target product; and according to the user emotion fusion features and a pre-established feature knowledge base, mining behavior pattern information and preference information of the user for the target product in a preset period, and storing the behavior pattern information and the preference information in a database of a BI system. Therefore, by adopting the embodiment of the invention, cross-platform data integration can be realized, and meanwhile, user behavior patterns and preferences can be found from a large amount of data.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method and device for online analysis of new media data. Background Art

[0002] In the new media era, companies and content creators increasingly rely on user interaction data on various new media platforms to guide product development and content creation. This data includes but is not limited to user comments on video content, live broadcast interactions, and product evaluation feedback. However, how to effectively use this data to gain valuable insights remains a challenge.

[0003] In related technologies, centralized data management methods are usually used, which mainly rely on a single database to store and analyze data collected from a specific new media platform. Due to the limitations of system design, these methods can usually only process data from a single platform and cannot achieve cross-platform data integration, resulting in one-sided analysis results. At the same time, these systems usually lack advanced data analysis and data mining functions, making it difficult to discover deep user behavior patterns and preferences from large amounts of data. Summary of the invention

[0004] The embodiments of the present application provide a method and device for online analysis of new media data. In order to have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0005] In a first aspect, an embodiment of the present application provides a new media data online analysis method, which is applied to a server of a BI system, and the method includes: When the time between the current moment and the last data analysis moment is equal to the preset period, user interaction data on the target product is collected from multiple new media platforms simultaneously through a preset distributed data collection architecture, where the preset distributed data collection architecture includes multiple data collection channels, each data collection channel is associated with a data collection interface provided by each new media platform; Extract emotional description information that can reflect the user's emotional inclination towards the target product from the user interaction data of each new media platform to obtain multi-source emotional description information; Input the multi-source emotion description information into the preset new media data online analysis component, and output the user emotion fusion features corresponding to the target product; Based on the user's emotional fusion features and the pre-established feature knowledge base, the user's behavior pattern information and preference information for the target product within a preset period are mined; In the database of the BI system, the ternary mapping relationship between the target product and the behavior pattern information and preference information is stored.

[0006] In a second aspect, an embodiment of the present application provides a new media data online analysis device, the device comprising: A data collection module, for collecting user interaction data about a target product from multiple new media platforms simultaneously through a preset distributed data collection architecture when the time between the current moment and the last data analysis moment is equal to a preset period, wherein the preset distributed data collection architecture includes multiple data collection channels, and each data collection channel is associated with a data collection interface provided by each new media platform one by one; The emotional description information extraction module is used to extract emotional description information that can reflect the user's emotional inclination towards the target product from the user interaction data of each new media platform, and obtain multi-source emotional description information; A data input module is used to input multi-source emotion description information into a preset new media data online analysis component and output user emotion fusion features corresponding to the target product; The data output module is used to mine the user's behavior pattern information and preference information for the target product within a preset period based on the user's emotion fusion features and the pre-established feature knowledge base; The ternary mapping relationship storage module is used to store the ternary mapping relationship between the target product and the behavior pattern information and the preference information in the database of the BI system.

[0007] The technical solution provided by the embodiments of the present application may have the following beneficial effects: In the embodiments of the present application, on the one hand, data collection on multiple platforms is performed through a preset distributed data collection architecture. Since the architecture includes multiple data collection channels, each channel is associated with the data collection interface of each new media platform one by one, data collection across platforms can be achieved simultaneously, thereby improving the coverage and diversity of data. On the other hand, emotional description information is extracted from the user interaction data of each new media platform and input into the preset new media data online analysis component, and the behavior pattern information and preference information are mined in combination with the pre-established feature knowledge base. The emotional description information can comprehensively characterize the user's emotional tendencies. The preset new media data online analysis component can process multimodal data, effectively integrate the complex interactive relationships between different types of data, and extract effective emotional features, so that the user's behavior patterns and preferences can be deeply discovered in cooperation with the pre-established feature knowledge base, thereby providing guidance data for product development and content creation for the business layer.

[0008] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0010] Figure 1 This is a schematic diagram of a method flow of a new media data online analysis method provided by an embodiment of the present application; Figure 2 It is a data schematic diagram of emotional description information reflecting the user's emotional tendency towards a target product provided by an embodiment of the present application; Figure 3 It is a schematic diagram of a component architecture of a new media data online analysis component provided in an embodiment of the present application; Figure 4 This is a data schematic diagram of a data table of a comprehensive emotion intensity value of each new media platform and each tendency theme provided by an embodiment of the present application; Figure 5 It is a data schematic diagram of a pre-established feature knowledge base provided in an embodiment of the present application; Figure 6 It is a data schematic diagram of a ternary mapping relationship provided in an embodiment of the present application; Figure 7 It is a flowchart of a method for training a sentiment tendency recognition model provided in an embodiment of the present application; Figure 8 It is a schematic diagram of a model architecture of a sentiment tendency recognition model provided in an embodiment of the present application; Fig. 9 It is a structural schematic diagram of a new media data online analysis device provided in an embodiment of the present application; Fig.10 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The following description and the accompanying drawings sufficiently illustrate specific embodiments of the present application to enable those skilled in the art to practice them.

[0012] It should be clear that 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 ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0013] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0014] In the description of the present application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. In addition, in the description of the present application, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0015] Currently, the creation of video content mainly relies on a centralized data management approach, which mainly relies on a single database to store and analyze data collected from specific new media platforms.

[0016] The inventors realize that these methods can usually only process data from a single platform and cannot achieve cross-platform data integration, resulting in one-sided analysis results. At the same time, these systems usually lack advanced data analysis and data mining capabilities, making it difficult to discover deep user behavior patterns and preferences from large amounts of data.

[0017] In order to solve the above problems, the present application provides a new media data online analysis method and device to solve the problems existing in the above related technical problems. In the embodiment of the present application, on the one hand, multi-platform data collection is performed through a preset distributed data collection architecture. Since the architecture includes multiple data collection channels, each channel is associated with the data collection interface of each new media platform one by one, so that data collection across platforms can be realized at the same time, which improves the coverage and diversity of data. On the other hand, emotional description information is extracted from the user interaction data of each new media platform and input into the preset new media data online analysis component, and the behavior pattern information and preference information are mined in combination with the pre-established feature knowledge base. The emotional description information can fully characterize the user's emotional tendency. The preset new media data online analysis component can process multimodal data, effectively integrate the complex interactive relationships between different types of data, and extract effective emotional features, so that the user's behavior patterns and preferences can be deeply discovered in cooperation with the pre-established feature knowledge base, thereby providing guidance data for product development and content creation for the business layer. The following uses an exemplary embodiment for detailed description.

[0018] The following will be combined with the attached Figure 1 -Attached Figure 8 , the online analysis method of new media data provided by the embodiment of the present application is introduced in detail. The method can be implemented by a computer program and can be run on an online analysis device of new media data based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application.

[0019] See also Figure 1 , which is a flow chart of a method for online analysis of new media data provided by the present application, and is applied to the server of a BI system. Figure 1 As shown, the method of the embodiment of the present application may include the following steps: S101, when the time between the current moment and the last data analysis moment is equal to a preset period, user interaction data about a target product is collected from multiple new media platforms simultaneously through a preset distributed data collection architecture, wherein the preset distributed data collection architecture includes multiple data collection channels, and each data collection channel is associated with a data collection interface provided by each new media platform; Among them, the preset period refers to a set time interval that specifies when to collect data from the new media platform. For example, the preset period can be once a day, a week, or a month. The distributed data collection architecture is a design of a data collection system that allows data to be collected from multiple data sources at the same time. This architecture is usually used to process large amounts of data and improve the efficiency of data collection. In the distributed data collection architecture, each data collection channel is an independent data collection path responsible for collecting data from a specific new media platform. New media platform: refers to an online platform where users can interact and communicate, such as social media, video sharing sites, blogs, etc. The data collection interface is a technical interface provided by the new media platform that allows external systems to collect data according to the platform's specifications and restrictions.

[0020] In some embodiments of the present application, the preset period can be set to once a week, that is, user interaction data is collected once a week. A distributed data collection architecture is designed, which includes multiple data collection channels, each of which is responsible for collecting data from different new media platforms. A data collection channel is set for each new media platform (such as Weibo, Douyin, TikTok, YouTube, etc.). Each data collection channel is associated with the data collection interface of the corresponding platform to ensure that data can be collected in accordance with the platform's regulations.

[0021] In some embodiments of the present application, the server end applied to the BI system determines the target product (such as a wheelchair) and calculates the duration between the current moment and the last data analysis moment in real time. If the duration is equal to a preset period, user interaction data about the wheelchair, such as video comments, live broadcast comments, product feedback, etc., are collected simultaneously from multiple new media platforms.

[0022] For example, when the preset period arrives (e.g., 8 a.m. every Monday), the distributed data collection architecture is automatically started, and all data collection channels start working at the same time. Each channel collects user interaction data about the target smartphone model from the associated new media platform, including comments, likes, shares, etc.

[0023] S102, extracting emotional description information that can reflect the user's emotional inclination towards the target product from the user interaction data of each new media platform, and obtaining multi-source emotional description information; Among them, the user interaction data of each new media platform includes video comments, live broadcast comments, and product feedback.

[0024] In some embodiments of the present application, the specific process of extracting emotional description information that can reflect the user's emotional inclination towards a target product from the user interaction data of each new media platform includes: performing data preprocessing on the video comments, live broadcast comments, and product opinion feedback included in the user interaction data of each new media platform to obtain the vocabulary sequence and phrase sequence of each new media platform; the preprocessing includes data cleaning, data deduplication, data formatting, and data segmentation; inputting the vocabulary sequence and phrase sequence of each new media platform into a pre-trained emotional inclination recognition model, and outputting multiple emotional words corresponding to the user interaction data of each new media platform and the strength of each emotional word; calculating the emotional intensity value of the user interaction data of each new media platform based on the strength of each emotional word and the number of emotional words; performing topic analysis on the vocabulary sequence and phrase sequence of each new media platform to extract the tendency themes involved in the user interaction data of each new media platform; using the emotional intensity value and tendency themes of the user interaction data of each new media platform as emotional description information that can reflect the user's emotional inclination towards the target product.

[0025] Among them, video comments are written feedback from users on video content, which may contain emotional expressions and evaluations of video content. Live comments are comments posted by users in real time during live broadcasts, which usually reflect users' immediate reactions to live content. Product feedback is user feedback on product experience, which may include suggestions, complaints or praises, and is an important source of information for product improvement. The sentiment tendency recognition model is a data analysis model used to automatically identify and classify sentiment tendencies in text data, such as positive, negative or neutral. Sentiment words are words that express specific emotions in text, such as "like", "disappointed", etc. Sentiment intensity is the intensity of the emotion expressed by the sentiment word, which can be weak, medium or strong. The sentiment intensity value is a numerical value used to quantify the intensity of the emotion in the text data. The tendency topic is the main focus or discussion focus reflected in the text data, representing the main interests or areas of concern of the user.

[0026] Among them, the emotional description information that reflects the user's emotional inclination towards the target product is, for example, Figure 2 As shown. User ID is the unique identifier of the user participating in the interaction. Interaction content is the specific content of user interaction, usually in text form, reflecting the user's views and feelings about the target product. The sentiment intensity value is the value obtained after sentiment analysis of the user interaction content, indicating the intensity of the emotion. The higher the value, the stronger the emotion. The tendency topic is the main focus or discussion focus reflected in the user interaction content, representing the user's attention to a specific aspect of the product.

[0027] In the embodiment of the present application, user interaction data is collected and analyzed from the new media platform to gain a deeper understanding of the user's emotional tendencies and concerns about the product. Data preprocessing ensures the quality of the data, and the emotional tendency recognition model and topic analysis provide in-depth insights into user emotions and interests.

[0028] S103, inputting the multi-source emotion description information into a preset new media data online analysis component, and outputting the user emotion fusion features corresponding to the target product; Among them, the multi-source emotion description information includes the emotion intensity value and tendency theme of the user interaction data of each new media platform. The tendency theme is used to reflect the user's concerns or the focus of user discussion.

[0029] For example Figure 3 As shown, the preset new media data online analysis component includes an emotion intensity value mapping module, a parameter acquisition module, a weight distribution module, a comprehensive emotion intensity value calculation module, and a user emotion fusion feature generation module.

[0030] In some embodiments of the present application, the multi-source emotion description information is input into the preset new media data online analysis component, and the specific process of outputting the user emotion fusion feature corresponding to the target product includes: the emotion intensity value mapping module standardizes the emotion intensity value to map the emotion intensity value to a unified scoring range to obtain the standardized emotion intensity of each new media platform; the parameter acquisition module obtains the user base, influence quantification parameters and comprehensive attention of the target product on each platform of each new media platform; the weight allocation module allocates weights to each new media platform according to the user base, influence quantification parameters and comprehensive attention; the comprehensive emotion intensity value calculation module calculates the comprehensive emotion intensity value of each new media platform based on the weight allocated to each new media platform and the standardized emotion intensity of each new media platform; the user emotion fusion feature generation module generates the user emotion fusion feature corresponding to the target product according to the comprehensive emotion intensity value of each new media platform and the tendency theme of each new media platform.

[0031] Among them, the sentiment intensity value refers to the value obtained through sentiment analysis, which indicates the intensity of user sentiment, usually ranging from negative values ​​(very negative) to positive values ​​(very positive). Standardization refers to the process of converting data into a standard range, such as 0 to 1 or -1 to 1, to facilitate comparison between different data sets. The user base refers to the number of users who follow or use the target product on a specific new media platform. The influence quantification parameter refers to the indicator that measures the influence of the new media platform, such as user activity, content sharing rate, etc. The comprehensive attention refers to the overall attention received by the target product on a specific new media platform, including the frequency and intensity of user interaction. Weight allocation refers to assigning a weight to each new media platform that reflects its importance based on the user base, influence quantification parameter and comprehensive attention. The comprehensive sentiment intensity value refers to the standardized sentiment intensity value after taking into account the weight, reflecting the weighted average of user sentiment on each platform. The user sentiment fusion feature refers to the comprehensive feature that integrates user sentiment on all new media platforms, which is used to reflect the overall emotional tendency of users towards the target product.

[0032] For example, the sentiment intensity values ​​are normalized to map them to a uniform scoring range of 0 to 1. Weights are assigned to each platform based on the user base, influence quantification parameters, and comprehensive attention. For example, if Weibo has a large user base and high activity, it may be assigned a higher weight. Based on the weights assigned to each platform and the standardized sentiment intensity values, the comprehensive sentiment intensity value of each platform is calculated. For example, if Weibo's standardized sentiment intensity value is 0.7 and its weight is 0.5, its comprehensive sentiment intensity value is 0.35. Based on the comprehensive sentiment intensity value of each platform and the tendency topics (such as camera quality, battery life, etc.), a user sentiment fusion feature corresponding to the target product is generated. This feature can be a comprehensive score or a classification label that reflects the user's overall emotional tendency towards smartphone models.

[0033] In some embodiments of the present application, the specific process of generating user emotion fusion features corresponding to the target product based on the comprehensive emotion intensity value of each new media platform and the tendency theme of each new media platform includes: associating the comprehensive emotion intensity value of each new media platform with each tendency theme to obtain multiple comprehensive emotion intensity values ​​of each tendency theme; summing the multiple comprehensive emotion intensity values ​​of each tendency theme to obtain an overall score of each tendency theme; performing feature conversion on tendency themes whose overall scores are greater than a preset threshold to obtain a tendency theme feature sequence; and splicing the tendency theme feature sequence into a whole as the user emotion fusion feature corresponding to the target product.

[0034] For example, if the target product is a smartwatch, then the multiple comprehensive emotion intensity values ​​of each tendency theme are summed up to obtain the overall score of each tendency theme. The tendency themes with an overall score greater than a preset threshold (e.g., 0.5) are subjected to feature conversion to obtain a tendency theme feature sequence. The tendency theme feature sequence is spliced ​​into a whole as the user emotion fusion feature corresponding to the smartwatch.

[0035] In some embodiments, the comprehensive sentiment intensity value of each new media platform and the data table of each tendency topic are as follows: Figure 4 As shown in the figure, the target product is a smartwatch. At this time, the battery life is 0.6 (Weibo) + 0.5 (YouTube) = 1.1, the design is 0.8 (Douyin), and the functional practicality is 0.7 (TikTok). All overall scores are greater than the preset threshold of 0.5, so all the tendency topics are converted into features. At this time, the tendency topic feature sequence is [battery life (1.1), design is beautiful (0.8), functional practicality (0.7)].

[0036] S104, mining the user's behavior pattern information and preference information for the target product within a preset period based on the user's emotion fusion features and a pre-established feature knowledge base; Among them, the user emotion fusion feature is a sequence of tendency theme features, and the pre-established feature knowledge base includes the association relationship between the original features and the behavior pattern information and preference information.

[0037] In some embodiments of the present application, the specific process of mining the user's behavior pattern information and preference information for the target product within a preset period based on the user's emotion fusion characteristics and a pre-established feature knowledge base is as follows: calculating the feature similarity between the tendency theme feature sequence and the original feature; taking the original feature whose similarity is greater than a preset similarity threshold as the target feature; obtaining the pattern information and preference information corresponding to the target feature from the association relationship; taking the pattern information and preference information corresponding to the target feature as the user's behavior pattern information and preference information for the target product within the preset period.

[0038] For example, the feature similarity between the topic feature sequence and the original feature is calculated by the cosine similarity method, and the original feature with a similarity greater than a preset similarity threshold (for example, 0.5) is used as the target feature. If the similarity of "energy-saving effect" is 0.6, then "energy-saving effect" is the target feature. From the pre-established feature knowledge base, obtain the pattern information and preference information corresponding to the target feature. If "energy-saving effect" is the target feature, the knowledge base contains information that users have a higher willingness to buy products with significant energy-saving effects, which can be used as behavior pattern information and preference information.

[0039] In some embodiments of the present application, the specific process of generating a pre-established feature knowledge base includes: collecting original user interaction data from multiple new media platforms; using natural language processing technology to analyze the original user interaction data to extract emotional features and tendency theme features in the original user interaction data, where the emotional features include positive emotional features, negative emotional features, and neutral emotional features; positive emotional features are used to characterize the user's optimism, negative emotional features are used to characterize the user's negative emotions, neutral emotional features are used to characterize the user's lack of emotions, and tendency theme features are used to reflect the user's discussion focus or concerns; based on the emotional features, analyzing the user's current pattern information and preference information for the product; storing the tendency theme features and the user's pattern information and preference information for different products to obtain a pre-established feature knowledge base. A pre-established feature knowledge base, for example Figure 5 shown.

[0040] S105, storing the ternary mapping relationship between the target product and the behavior pattern information and the preference information in the database of the BI system.

[0041] In some embodiments of the present application, the target product is determined to be a newly released smart watch. At this time, after obtaining the user's behavior pattern information and preference information for the target product within a preset period based on the pre-established feature knowledge base, a ternary mapping relationship can be established for the target product (smart watch), behavior pattern information (purchase tendency) and preference information (long battery life, beautiful design). The ternary mapping relationship is for example Figure 6 shown.

[0042] In the embodiments of the present application, on the one hand, data collection on multiple platforms is performed through a preset distributed data collection architecture. Since the architecture includes multiple data collection channels, each channel is associated with the data collection interface of each new media platform one by one, data collection across platforms can be achieved simultaneously, thereby improving the coverage and diversity of data. On the other hand, emotional description information is extracted from the user interaction data of each new media platform and input into the preset new media data online analysis component, and the behavior pattern information and preference information are mined in combination with the pre-established feature knowledge base. The emotional description information can comprehensively characterize the user's emotional tendencies. The preset new media data online analysis component can process multimodal data, effectively integrate the complex interactive relationships between different types of data, and extract effective emotional features, so that the user's behavior patterns and preferences can be deeply discovered in cooperation with the pre-established feature knowledge base, thereby providing guidance data for product development and content creation for the business layer.

[0043] See also Figure 7 , which is a flow chart of a method for training a sentiment tendency recognition model according to an embodiment of the present application. Figure 7 As shown, the method of the embodiment of the present application may include the following steps: S201, collect historical video comments, historical live broadcast comments, and historical product feedback covering different fields and sentiment tendencies to obtain historical text data; S202, sentiment annotation is performed on the collected historical text data to divide the historical text data into positive sentiment data, negative sentiment data and neutral sentiment data; the positive sentiment data is data annotated to represent the user's optimistic sentiment, the negative sentiment data is data annotated to represent the user's negative sentiment, and the neutral sentiment data is data annotated to represent the user's lack of sentiment; In some embodiments, the sentiment tendency of each comment is identified and marked, positive sentiment may be represented by "1", negative sentiment by "-1", and neutral sentiment by "0", thereby obtaining positive sentiment data, negative sentiment data, and neutral sentiment data.

[0044] S203, creating a sentiment tendency recognition model; For example Figure 8 As shown in the figure, the sentiment tendency recognition model includes a word embedding network, a sequence embedding layer, a position embedding layer, a self-attention layer, a feature fusion module and a loss function.

[0045] S204, inputting the positive emotion data, the negative emotion data and the neutral emotion data into the emotion tendency recognition model, and outputting the loss value of the model; In some embodiments of the present application, positive sentiment data, negative sentiment data and neutral sentiment data are input into a sentiment tendency recognition model, and the specific process of outputting the loss value of the model includes: performing data cleaning and text segmentation on the positive sentiment data, negative sentiment data and neutral sentiment data to obtain text data after segmentation; inputting the text data after segmentation into a word embedding network to capture the semantic relationship and context information between words for vocabulary conversion to obtain word vectors in a text sequence; inputting the word vectors in the text sequence into a sequence embedding layer to capture the order information and context relationship in the text sequence to obtain a sequence vector; obtaining the position information of the text data after segmentation, and inputting the obtained position information of the text data into a position embedding layer to capture the position information of the words in the text to obtain a position vector; inputting the sequence vector and the position vector into a self-attention layer to capture the relationship between different words to obtain global features; inputting the word vector, sequence vector, position vector and global features into a feature fusion module to integrate feature information at different levels to obtain a comprehensive feature vector; outputting the loss value of the model based on the comprehensive feature vector and the loss function.

[0046] Specifically, according to the comprehensive feature vector and the loss function, the specific process of outputting the loss value of the model includes: performing a linear transformation on the comprehensive feature vector to obtain a score vector for each emotion category; converting the score vector for each emotion category into a probability distribution to obtain a predicted emotion category probability distribution; inputting the predicted emotion category probability distribution and the annotated emotion label corresponding to the predicted emotion category probability distribution into the loss function, and outputting the loss value of the model; The calculation formula of the score vector is:

[0047] in, is the score vector, is the weight matrix, is the comprehensive feature vector, is the bias vector; The calculation formula of probability distribution is:

[0048] in, It is The probability distribution of emotion categories, It is The score vector of sentiment categories, is the total number of sentiment categories, is the sum of the score vectors of all sentiment categories; Among them, the loss function is:

[0049] in, is the sentiment tendency recognition model at time step The forward propagation result is Update the parameters for the model, The optimization goal is to maximize the set of parameters The loss value, is the predicted probability distribution of sentiment categories, is the labeled emotion label corresponding to the predicted emotion category probability distribution, is the probability distribution of all predicted emotion categories, Indicates the emotional label The quantitative value of is the logarithmic probability, are the original parameters of the model, are the updated model parameters, is the time step .

[0050] S205, when the loss value reaches the minimum, a pre-trained sentiment tendency recognition model is generated; or, when the loss value has not reached the minimum, the model loss value is forward propagated to update the model update parameters of the sentiment tendency recognition model, and the steps of inputting positive sentiment data, negative sentiment data and neutral sentiment data into the sentiment tendency recognition model are continued until the loss value reaches the minimum.

[0051] In the embodiments of the present application, on the one hand, data collection on multiple platforms is performed through a preset distributed data collection architecture. Since the architecture includes multiple data collection channels, each channel is associated with the data collection interface of each new media platform one by one, data collection across platforms can be achieved simultaneously, thereby improving the coverage and diversity of data. On the other hand, emotional description information is extracted from the user interaction data of each new media platform and input into the preset new media data online analysis component, and the behavior pattern information and preference information are mined in combination with the pre-established feature knowledge base. The emotional description information can comprehensively characterize the user's emotional tendencies. The preset new media data online analysis component can process multimodal data, effectively integrate the complex interactive relationships between different types of data, and extract effective emotional features, so that the user's behavior patterns and preferences can be deeply discovered in cooperation with the pre-established feature knowledge base, thereby providing guidance data for product development and content creation for the business layer.

[0052] The following are device embodiments of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0053] See also Fig. 9 , which shows a schematic diagram of the structure of a new media data online analysis device provided by an exemplary embodiment of the present application. The new media data online analysis device can be implemented as all or part of an electronic device through software, hardware or a combination of both. The device 1 includes a data collection module 10, an emotion description information extraction module 20, a data input module 30, a data output module 40, and a ternary mapping relationship storage module 50.

[0054] The data collection module 10 is used to collect user interaction data about the target product from multiple new media platforms simultaneously through a preset distributed data collection architecture when the time between the current moment and the last data analysis moment is equal to a preset period. The preset distributed data collection architecture includes multiple data collection channels, and each data collection channel is associated with a data collection interface provided by each new media platform one by one; The emotion description information extraction module 20 is used to extract emotion description information that can reflect the user's emotional inclination towards the target product from the user interaction data of each new media platform, and obtain multi-source emotion description information; The data input module 30 is used to input the multi-source emotion description information into the preset new media data online analysis component, and output the user emotion fusion features corresponding to the target product; The data output module 40 is used to mine the user's behavior pattern information and preference information for the target product within a preset period according to the user's emotion fusion characteristics and the pre-established characteristic knowledge base; The ternary mapping relationship storage module 50 is used to store the ternary mapping relationship between the target product and the behavior pattern information and the preference information in the database of the BI system.

[0055] It should be noted that the new media data online analysis device provided in the above embodiment only uses the division of the above functional modules as an example when executing the new media data online analysis method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the new media data online analysis device provided in the above embodiment and the new media data online analysis method embodiment belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0056] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0057] In the embodiments of the present application, on the one hand, data collection on multiple platforms is performed through a preset distributed data collection architecture. Since the architecture includes multiple data collection channels, each channel is associated with the data collection interface of each new media platform one by one, data collection across platforms can be achieved simultaneously, thereby improving the coverage and diversity of data. On the other hand, emotional description information is extracted from the user interaction data of each new media platform and input into the preset new media data online analysis component, and the behavior pattern information and preference information are mined in combination with the pre-established feature knowledge base. The emotional description information can comprehensively characterize the user's emotional tendencies. The preset new media data online analysis component can process multimodal data, effectively integrate the complex interactive relationships between different types of data, and extract effective emotional features, so that the user's behavior patterns and preferences can be deeply discovered in cooperation with the pre-established feature knowledge base, thereby providing guidance data for product development and content creation for the business layer.

[0058] The present application also provides a computer-readable medium having program instructions stored thereon, and when the program instructions are executed by a processor, the online analysis method for new media data provided by the above-mentioned various method embodiments is implemented.

[0059] The present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the new media data online analysis method of each of the above method embodiments.

[0060] See also Fig.10 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Fig.10 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0061] The communication bus 1002 is used to realize the connection and communication between these components.

[0062] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0063] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0064] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts of the entire electronic device 1000, and executes various functions and processes data of the electronic device 1000 by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0065] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may also be optionally at least one storage system located away from the aforementioned processor 1001. As Fig.10 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a new media data online analysis application.

[0066] exist Fig.10 In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call the new media data online analysis application stored in the memory 1005, and specifically perform the following operations: When the time between the current moment and the last data analysis moment is equal to the preset period, user interaction data on the target product is collected from multiple new media platforms simultaneously through a preset distributed data collection architecture, where the preset distributed data collection architecture includes multiple data collection channels, each data collection channel is associated with a data collection interface provided by each new media platform; Extract emotional description information that can reflect the user's emotional inclination towards the target product from the user interaction data of each new media platform to obtain multi-source emotional description information; Input the multi-source emotion description information into the preset new media data online analysis component, and output the user emotion fusion features corresponding to the target product; Based on the user's emotional fusion features and the pre-established feature knowledge base, the user's behavior pattern information and preference information for the target product within a preset period are mined; In the database of the BI system, the ternary mapping relationship between the target product and the behavior pattern information and preference information is stored.

[0067] In one embodiment, when the processor 1001 inputs the multi-source emotion description information into the preset new media data online analysis component and outputs the user emotion fusion feature corresponding to the target product, the processor 1001 specifically performs the following operations: The emotion intensity value mapping module normalizes the emotion intensity value to map the emotion intensity value to a unified scoring range, thereby obtaining the standardized emotion intensity of each new media platform; The parameter acquisition module obtains the user base of each new media platform, the quantitative parameters of influence, and the comprehensive attention of the target product on each platform; The weight allocation module allocates weights to each new media platform based on the user base, influence quantitative parameters, and comprehensive attention; The comprehensive emotion intensity value calculation module calculates the comprehensive emotion intensity value of each new media platform based on the weight assigned to each new media platform and the standardized emotion intensity of each new media platform; The user emotion fusion feature generation module generates the user emotion fusion features corresponding to the target product according to the comprehensive emotion intensity value of each new media platform and the tendency theme of each new media platform.

[0068] In one embodiment, when the processor 1001 generates the user emotion fusion feature corresponding to the target product according to the comprehensive emotion intensity value of each new media platform and the tendency theme of each new media platform, the processor 1001 specifically performs the following operations: Associating the comprehensive sentiment intensity value of each new media platform with each tendency topic to obtain multiple comprehensive sentiment intensity values ​​of each tendency topic; The multiple comprehensive sentiment intensity values ​​of each tendency topic are summed up to obtain the overall score of each tendency topic; Perform feature conversion on the tendency topics whose overall scores are greater than a preset threshold to obtain a tendency topic feature sequence; The tendency theme feature sequences are spliced ​​into a whole as the user emotion fusion features corresponding to the target product.

[0069] In one embodiment, when the processor 1001 mines the user's behavior pattern information and preference information for the target product within a preset period according to the user's emotion fusion feature and the pre-established feature knowledge base, the processor 1001 specifically performs the following operations: Calculate the feature similarity between the tendency topic feature sequence and the original feature; The original features whose similarity is greater than the preset similarity threshold are taken as target features; From the association relationship, obtain the pattern information and preference information corresponding to the target feature; The pattern information and preference information corresponding to the target feature are used as the user's behavior pattern information and preference information for the target product within a preset period.

[0070] In one embodiment, when the processor 1001 generates a pre-established feature knowledge base, the processor 1001 specifically performs the following operations: Collect raw user interaction data from multiple new media platforms; Use natural language processing technology to analyze the original user interaction data to extract emotional features and tendency theme features from the original user interaction data. Emotional features include positive emotional features, negative emotional features, and neutral emotional features. Positive emotional features are used to represent the user's optimistic emotions, negative emotional features are used to represent the user's negative emotions, neutral emotional features are used to represent the user's lack of emotions, and tendency theme features are used to reflect the user's discussion focus or concerns. Analyze the user's current product pattern information and preference information based on emotional characteristics; Store the tendency topic features and the user's pattern information and preference information for different products to obtain a pre-established feature knowledge base.

[0071] In one embodiment, when the processor 1001 extracts the emotional description information that can reflect the user's emotional inclination toward the target product from the user interaction data of each new media platform, the processor 1001 specifically performs the following operations: Data preprocessing is performed on the user interaction data of each new media platform, including video comments, live broadcast comments, and product feedback, to obtain the vocabulary sequence and phrase sequence of each new media platform; preprocessing includes data cleaning, data deduplication, data formatting, and data segmentation; Input the word sequence and phrase sequence of each new media platform into the pre-trained sentiment tendency recognition model, and output multiple sentiment words corresponding to the user interaction data of each new media platform and the strength of each sentiment word; Calculate the sentiment intensity value of user interaction data of each new media platform based on the intensity of each sentiment word and the number of sentiment words; Conduct topic analysis on the word sequences and phrase sequences of each new media platform to extract the tendency topics involved in the user interaction data of each new media platform; The emotional intensity value and tendency theme of the user interaction data of each new media platform are used as emotional description information that can reflect the user's emotional tendency towards the target product.

[0072] In one embodiment, when the processor 1001 generates a pre-trained emotion tendency recognition model, the processor 1001 specifically performs the following operations: Collect historical video comments, historical live broadcast comments, and historical product feedback covering different fields and sentiment tendencies to obtain historical text data; The collected historical text data is emotionally annotated to divide the historical text data into positive emotional data, negative emotional data and neutral emotional data; positive emotional data is data annotated to represent the user's optimistic emotions, negative emotional data is data annotated to represent the user's negative emotions, and neutral emotional data is data annotated to represent the user's lack of emotions; Create a sentiment tendency recognition model; Input positive emotion data, negative emotion data and neutral emotion data into the emotion tendency recognition model and output the loss value of the model; When the loss value reaches the minimum, a pre-trained sentiment tendency recognition model is generated; alternatively, when the loss value does not reach the minimum, the model loss value is forward propagated to update the model update parameters of the sentiment tendency recognition model, and the steps of inputting positive sentiment data, negative sentiment data, and neutral sentiment data into the sentiment tendency recognition model are continued until the loss value reaches the minimum.

[0073] In one embodiment, when the processor 1001 inputs the positive emotion data, the negative emotion data, and the neutral emotion data into the emotion tendency recognition model and outputs the loss value of the model, the following operations are specifically performed: Perform data cleaning and text segmentation on positive sentiment data, negative sentiment data, and neutral sentiment data to obtain text data after segmentation; The segmented text data is input into the word embedding network to capture the semantic relationship and context information between words for word conversion, and obtain the word vector in the text sequence; The word vectors in the text sequence are input into the sequence embedding layer to capture the order information and contextual relationship in the text sequence and obtain the sequence vector; Obtaining the position information of the text data after word segmentation, and inputting the obtained position information of the text data into the position embedding layer to capture the position information of the vocabulary in the text and obtain the position vector; The sequence vector and position vector are input into the self-attention layer to capture the relationship between different words and obtain global features; The word vector, sequence vector, position vector and global feature are input into the feature fusion module to integrate feature information at different levels and obtain a comprehensive feature vector. According to the comprehensive feature vector and loss function, the loss value of the model is output.

[0074] In one embodiment, when the processor 1001 outputs the loss value of the model according to the comprehensive feature vector and the loss function, the processor 1001 specifically performs the following operations: Perform linear transformation on the comprehensive feature vector to obtain the score vector for each emotion category; Convert the score vector of each emotion category into a probability distribution to obtain the predicted emotion category probability distribution; The predicted emotion category probability distribution and the annotated emotion label corresponding to the predicted emotion category probability distribution are input into the loss function, and the loss value of the model is output.

[0075] In the embodiments of the present application, on the one hand, data collection on multiple platforms is performed through a preset distributed data collection architecture. Since the architecture includes multiple data collection channels, each channel is associated with the data collection interface of each new media platform one by one, data collection across platforms can be achieved simultaneously, thereby improving the coverage and diversity of data. On the other hand, emotional description information is extracted from the user interaction data of each new media platform and input into the preset new media data online analysis component, and the behavior pattern information and preference information are mined in combination with the pre-established feature knowledge base. The emotional description information can comprehensively characterize the user's emotional tendencies. The preset new media data online analysis component can process multimodal data, effectively integrate the complex interactive relationships between different types of data, and extract effective emotional features, so that the user's behavior patterns and preferences can be deeply discovered in cooperation with the pre-established feature knowledge base, thereby providing guidance data for product development and content creation for the business layer.

[0076] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program for online analysis of new media data can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium of the program for online analysis of new media data can be a magnetic disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0077] 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. A new media data online analysis method, characterized in that: Applied to the server side of the BI system, the method includes: When the time between the current moment and the last data analysis moment is equal to the preset period, user interaction data on the target product is collected from multiple new media platforms simultaneously through a preset distributed data collection architecture, wherein the preset distributed data collection architecture includes multiple data collection channels, each data collection channel is associated with a data collection interface provided by each new media platform; Extracting emotional description information that can reflect the user's emotional inclination towards the target product from the user interaction data of each new media platform to obtain multi-source emotional description information; Inputting the multi-source emotion description information into a preset new media data online analysis component, and outputting the user emotion fusion features corresponding to the target product; Mining the user's behavior pattern information and preference information for the target product within a preset period based on the user's emotion fusion features and a pre-established feature knowledge base; In the database of the BI system, a ternary mapping relationship between the target product and the behavior pattern information and the preference information is stored.

2. The method according to claim 1, characterized in that The multi-source emotion description information includes the emotion intensity value and the tendency theme of the user interaction data of each new media platform, and the tendency theme is used to reflect the user's focus or the focus of user discussion; the preset new media data online analysis component includes an emotion intensity value mapping module, a parameter acquisition module, a weight distribution module, a comprehensive emotion intensity value calculation module, and a user emotion fusion feature generation module; The step of inputting the multi-source emotion description information into a preset new media data online analysis component and outputting the user emotion fusion features corresponding to the target product includes: The emotion intensity value mapping module performs standardization processing on the emotion intensity value to map the emotion intensity value to a unified scoring range to obtain the standardized emotion intensity of each new media platform; The parameter acquisition module acquires the user base, influence quantification parameters and comprehensive attention of the target product on each platform of each new media platform; The weight allocation module allocates a weight to each new media platform according to the user base, the influence quantification parameter and the comprehensive attention; The comprehensive emotion intensity value calculation module calculates the comprehensive emotion intensity value of each new media platform based on the weight assigned to each new media platform and the standardized emotion intensity of each new media platform; The user emotion fusion feature generation module generates a user emotion fusion feature corresponding to the target product according to the comprehensive emotion intensity value of each new media platform and the tendency theme of each new media platform.

3. The method according to claim 2, characterized in that Generating the user emotion fusion feature corresponding to the target product according to the comprehensive emotion intensity value of each new media platform and the tendency theme of each new media platform includes: Associating the comprehensive emotion intensity value of each new media platform with each tendency topic to obtain multiple comprehensive emotion intensity values ​​of each tendency topic; Sum up the multiple comprehensive sentiment intensity values ​​of each tendency topic to obtain the overall score of each tendency topic; Performing feature conversion on the tendency topics whose overall scores are greater than a preset threshold to obtain a tendency topic feature sequence; The tendency theme feature sequences are spliced ​​into a whole as the user emotion fusion feature corresponding to the target product.

4. The method according to claim 3, characterized in that The user emotion fusion feature is a tendency theme feature sequence, and the pre-established feature knowledge base includes the association relationship between the original feature and the behavior pattern information and the preference information; The mining of the user's behavior pattern information and preference information for the target product within a preset period based on the user's emotion fusion feature and a pre-established feature knowledge base includes: Calculating the feature similarity between the tendency topic feature sequence and the original feature; The original features whose similarity is greater than a preset similarity threshold are taken as target features; From the association relationship, obtain the mode information and preference information corresponding to the target feature; The pattern information and preference information corresponding to the target feature are used as the user's behavior pattern information and preference information for the target product within a preset period.

5. The method according to claim 4, generating a pre-established feature knowledge base according to the following steps, comprising: Collect raw user interaction data from multiple new media platforms; Analyzing the original user interaction data using natural language processing technology to extract emotional features and tendency theme features in the original user interaction data, wherein the emotional features include positive emotional features, negative emotional features, and neutral emotional features; The positive emotion feature is used to characterize the user's optimistic emotion, the negative emotion feature is used to characterize the user's negative emotion, the neutral emotion feature is used to characterize the user's lack of emotion, and the tendency topic feature is used to reflect the user's discussion focus or concern; Analyze the user's current mode information and preference information for the product based on the emotional characteristics; The tendency theme features and the user's pattern information and preference information for different products are stored to obtain a pre-established feature knowledge base.

6. The method according to claim 1, characterized in that The user interaction data of each new media platform includes video comments, live broadcast comments, and product feedback; Extracting emotional description information that can reflect the user's emotional inclination toward the target product from the user interaction data of each new media platform includes: Preprocessing the user interaction data of each new media platform, including video comments, live broadcast comments, and product feedback, to obtain a vocabulary sequence and a phrase sequence of each new media platform; the preprocessing includes data cleaning, data deduplication, data formatting, and data segmentation; Inputting the vocabulary sequence and phrase sequence of each new media platform into a pre-trained sentiment tendency recognition model, and outputting a plurality of sentiment words corresponding to the user interaction data of each new media platform and the strength of each sentiment word; Calculating the emotion intensity value of the user interaction data of each new media platform according to the intensity of each emotion vocabulary and the number of emotion vocabulary; Performing topic analysis on the word sequence and phrase sequence of each new media platform to extract the tendency topics involved in the user interaction data of each new media platform; The emotional intensity value of the user interaction data of each new media platform and the tendency theme are used as emotional description information that can reflect the user's emotional tendency towards the target product.

7. The method according to claim 6, generating a pre-trained emotion tendency recognition model according to the following steps, comprising: Collect historical video comments, historical live broadcast comments, and historical product feedback covering different fields and sentiment tendencies to obtain historical text data; Perform sentiment annotation on the collected historical text data to classify the historical text data into positive sentiment data, negative sentiment data and neutral sentiment data; The positive emotion data is data labeled to represent the user's optimistic emotion, the negative emotion data is data labeled to represent the user's negative emotion, and the neutral emotion data is data labeled to represent the user's lack of emotion; Create a sentiment tendency recognition model; Inputting the positive emotion data, negative emotion data and neutral emotion data into the emotion tendency recognition model, and outputting the loss value of the model; When the loss value reaches the minimum, a pre-trained emotion tendency recognition model is generated; or, when the loss value does not reach the minimum, the model loss value is forward propagated to update the model update parameters of the emotion tendency recognition model, and the step of inputting the positive emotion data, negative emotion data and neutral emotion data into the emotion tendency recognition model is continued until the loss value reaches the minimum.

8. The method according to claim 7, characterized in that The sentiment tendency recognition model includes a word embedding network, a sequence embedding layer, a position embedding layer, a self-attention layer, a feature fusion module and a loss function; The step of inputting the positive emotion data, the negative emotion data and the neutral emotion data into the emotion tendency recognition model and outputting the loss value of the model comprises: Performing data cleaning and text segmentation on the positive sentiment data, the negative sentiment data, and the neutral sentiment data to obtain text data after segmentation; Inputting the segmented text data into the word embedding network to capture the semantic relationship and context information between words for word conversion, and obtaining word vectors in the text sequence; Inputting the word vectors in the text sequence into the sequence embedding layer to capture the order information and contextual relationship in the text sequence to obtain a sequence vector; Acquire the position information of the text data after word segmentation, and input the acquired position information of the text data into the position embedding layer to capture the position information of the vocabulary in the text and obtain the position vector; Inputting the sequence vector and the position vector into the self-attention layer to capture the relationship between different words and obtain a global feature; Inputting the word vector, sequence vector, position vector and global feature into the feature fusion module to integrate feature information at different levels to obtain a comprehensive feature vector; According to the comprehensive feature vector and the loss function, the loss value of the model is output.

9. The method according to claim 8, characterized in that Outputting the loss value of the model according to the comprehensive feature vector and the loss function includes: Performing a linear transformation on the comprehensive feature vector to obtain a score vector for each emotion category; Convert the score vector of each emotion category into a probability distribution to obtain a predicted emotion category probability distribution; The predicted emotion category probability distribution and the annotated emotion label corresponding to the predicted emotion category probability distribution are input into the loss function, and the loss value of the model is output.

10. A new media data online analysis device, characterized in that: The device comprises: A data collection module, for simultaneously collecting user interaction data on a target product from multiple new media platforms through a preset distributed data collection architecture when the time between the current moment and the last data analysis moment is equal to a preset period, wherein the preset distributed data collection architecture includes multiple data collection channels, and each data collection channel is associated with a data collection interface provided by each new media platform one by one; An emotion description information extraction module is used to extract emotion description information that can reflect the user's emotional inclination towards the target product from the user interaction data of each new media platform, and obtain multi-source emotion description information; A data input module, used to input the multi-source emotion description information into a preset new media data online analysis component, and output the user emotion fusion features corresponding to the target product; A data output module is used to mine the user's behavior pattern information and preference information for the target product within a preset period according to the user's emotion fusion feature and a pre-established feature knowledge base; The ternary mapping relationship storage module is used to store the ternary mapping relationship between the target product and the behavior pattern information and the preference information in the database of the BI system.

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