25 results about "Comment analysis" patented technology

The invention discloses a multi-modal self-adaptive online shopping comment authenticity and score credibility evaluation system, which belongs to the technical field of online shopping comment analysis and comprises an enhanced text semantic understanding module, an image authenticity verification module, a user behavior deep modeling module, a self-adaptive weight distribution module and a two-dimensional prediction module. According to the multi-modal self-adaptive online shopping comment authenticity and score credibility evaluation system, the false comment recognition capability is greatly improved, the misjudgment rate is effectively reduced, comment authenticity and score two-dimensional evaluation is achieved, the multi-modal fusion effect is better than that of simple feature splicing, high-concurrency processing is supported, complex scenes can be covered, and the online shopping comment authenticity and score credibility evaluation method is suitable for large-scale popularization and application. And the engineering practicability is high.

The invention discloses an AI-based game material comment analysis method, system and device and a medium, and the method specifically comprises the steps: carrying out the multi-dimensional sentiment analysis of current material comment data based on a special game sentiment dictionary, and outputting a sentiment tendency analysis result; constructing a player clustering model according to the emotional tendency analysis result and the game behavior data, and establishing a social behavior and comment association model according to the emotional tendency analysis result and the social behavior data; extracting positive and negative feedback information for different player groups according to the player clustering model and the association model; and calculating an optimization priority of each material dimension according to the positive and negative feedback information and a preset player value evaluation model, associating the negative feedback information of the high-value player group with abnormal points in the game behavior data, and generating a material iteration suggestion. According to the method, accurate game material comment analysis is realized, customized iteration suggestions are generated, and the game material optimization efficiency and quality are improved.

An object of a system according to an embodiment is to improve motivation by effectively transmitting a praise comment of a customer to a crew.SOLUTION: A system according to an embodiment includes a comment collection unit, a comment analysis unit, a comment generation unit, and a comment transmission unit. A comment collection part collects praise comments of customers. The comment analysis unit analyzes the praise comment collected by the comment collection unit. A comment generation part generates an appropriate praise comment on the basis of the content analyzed by the comment analysis part. The comment transmission unit transmits the praise comment generated by the comment generation unit to the crew.SELECTED DRAWING: Figure 1

The invention provides an information association analysis method and system based on network comment big data, and relates to the technical field of big data, and the method comprises the steps: firstly extracting instantaneous propagation fragments from the network comment big data, recognizing unit trigger nodes, extracting features, generating association units to form a comment association unit set, and extracting unit association information of the association units; analyzing an evolution influence relationship to form a comment association dynamic factor evolution sequence, taking an association unit as a topology node, determining dynamic association parameters and directions by utilizing a dynamic factor evolution process, constructing comment dynamic association topology, analyzing a comment dynamic association topology structure, integrating the comment dynamic association topology structure into a comment association evolution situation, and transmitting the comment association evolution situation to a terminal. Therefore, network comment association and dynamic evolution can be deeply analyzed, comment dynamic conditions can be mastered in time, scientific and reasonable decisions can be made, and comment analysis efficiency and accuracy are improved.

The embodiment of the invention discloses a data display method and device, equipment and a storage medium. By setting the comment analysis entry in the content display page corresponding to the target media content published by the content creator, a demander can directly trigger the entry to quickly obtain the comment analysis result of the target media content without manually browsing massive works and comments one by one, so that the operation cost and the time cost of feedback collection are greatly reduced; meanwhile, the comment analysis page directly presents emotional tendency data generated based on full comment content, so that the one-sidedness of manual subjective judgment and the influence of individual differences are avoided, and the comprehensiveness and objectivity of feedback evaluation are ensured; besides, by means of a screening control of a multi-user attribute dimension, a demand side can accurately position emotion feedback of a specific audience group, differential analysis of feedback of different user groups is realized, and the pertinence and accuracy of an evaluation result are further improved.

The application provides an information correlation analysis method and system based on network comment big data, relates to the technical field of big data, and first extracts a transient propagation segment from network comment big data, identifies a unit trigger node and extracts features, generates a correlation unit to form a comment correlation unit set, extracts unit correlation information of the correlation unit, analyzes an evolution influence relationship to form a comment correlation dynamic factor evolution sequence, uses the correlation unit as a topological node, determines dynamic correlation parameters and directions by using a dynamic factor evolution process, constructs a comment dynamic correlation topology, analyzes comment dynamic correlation topology structure integration into a comment correlation evolution trend and transmits the comment correlation evolution trend to a terminal, so that network comment correlation and dynamic evolution can be deeply analyzed, timely comment dynamics can be mastered, scientific and reasonable decisions can be made, and the efficiency and accuracy of comment analysis are improved.

The present application relates to a server and a demand analysis report generation method, the server comprising: a communication device configured to communicate with a data source; the at least one processor is connected with the communication device and is configured to obtain at least one piece of comment data for the target product from the data source; selecting a target agent for processing the comment data from the at least one agent according to the comment data; inputting the comment data into the target agent to obtain comment analysis data of the comment data; and generating a demand analysis report for the target product according to the comment analysis data corresponding to the at least one piece of comment data. The demand analysis report can be automatically generated according to the comment data of the target product, manual analysis of the comment data is not needed, and the analysis efficiency can be improved. And subjective deviation caused by manual analysis is reduced, and the analysis accuracy can be improved. As the demand analysis report is automatically generated, a lot of manpower and material resources are not needed, and the cost is reduced.

The application discloses a multi-level comment analysis method based on stance detection, and specifically as follows: comments published by users in a microblog are crawled, data cleaning is performed, and then a stance detection model constructed by the application is used to mark the comment stance. The application provides four kinds of analysis functions based on the stance, namely, a word cloud analysis based on the stance, a time sequence analysis based on the stance, a gender analysis based on the stance and a geographical position analysis based on the stance. According to the feedback of the user on the comment stance, the scheme can construct a new data set, and the new data set can be used to assist model training, so that the precision of the stance detection model is further improved.

The application discloses an intelligent public opinion analysis method and device, terminal equipment and a storage medium. The method comprises the following steps: collecting user video comment data; generating a concerned video image based on the collected user video comment data, and generating an intelligent public opinion preliminary analysis result based on the generated concerned video image; generating comment analysis information based on the collected user video comment data and the intelligent public opinion preliminary analysis result; and performing video public opinion analysis based on the intelligent public opinion preliminary analysis result and the comment analysis information. The intelligent public opinion analysis method based on comment collection has the advantages of lower cost, more comprehensive coverage, higher efficiency and higher accuracy compared with traditional methods.

An object of the system according to the embodiment is to efficiently collect and analyze NPS comments from a plurality of platforms on the Internet.SOLUTION: A system includes a comment collection part, a comment classification part, a comment analysis part, and a report generation part. The comment collector automatically collects NPS comments for MNOs and MVNOs from multiple platforms on the Internet. The comment classification component classifies the comments collected by the comment collection component into positive comments and negative comments. The comment analysis unit analyzes the comments classified by the comment classification unit in more detail. The report generation part generates a result report on the basis of the result analyzed by the comment analysis part.SELECTED DRAWING: Figure 1

The invention provides an aspect-level sentiment analysis method based on knowledge assistance and sentiment relation driving, and relates to the technical field of sentiment analysis, which comprises the steps of performing multi-task definition, constructing input representation, searching and weighting a common sense path based on an external knowledge base, constructing a common sense perception graph and updating node representation, and performing multi-task joint training. And finally realizing emotion prediction. According to the method, the sentiment relation between sentence sentiment fundamental tones and aspect words is introduced, the sentiment-emphasized perception ability of the model for commentators is enhanced, external common knowledge is fused, the semantic understanding and implicit relation reasoning ability is improved, and a multi-task learning framework is adopted; the method assists tasks in improving robustness and interpretability of aspect-level sentiment analysis through sentiment relation recognition, is suitable for actual scenes such as user comment analysis and product feedback mining, and helps enterprises and institutions deeply understand user sentiment tendency.

The invention discloses a new media business knowledge base, an excellent case precipitation system, a method and a medium, and constructs a brand-new new media business field knowledge base and an excellent case precipitation technology application which integrate the application of a traditional media technology, big data and a big model on excellent cases, and take an intelligent media fusion big model as a technical support point. Traditional new media business domain knowledge and excellent case precipitation seriously depend on professional media personnel and are greatly influenced by professional levels and working efficiency of the personnel, so that the problems that the number of excellent case works is small, types are single, comment analysis angles are the same, professional experience knowledge cannot be shared and inherited and the like exist. According to the technical application, a set of complete automatic application covering case collection, knowledge base management, case analysis and case release is provided from a large droop model, and the problems are effectively solved; the construction cost of a professional knowledge base is greatly reduced, the efficiency of creating excellent works by editors is improved, a reference, learning and communication platform is provided, and the high-quality development of the new media business field is promoted.

This application provides a method for processing public comments, an electronic device, a storage medium, and a program product. The method may include: obtaining public comments from at least one application, the public comments including attribute information of a target object; generating first prompt information based on the public comments; performing generation processing based on the first prompt information using a first generative basic model to obtain key content and emotional information corresponding to the public comments; if the emotional category in the emotional information is a first emotional category, generating second prompt information based on the key content; performing generation processing based on the second prompt information using a second generative basic model to obtain first interactive information; and feeding back the first interactive information to the application from which the public comments originated. This improves the efficiency of public comment analysis and enhances the user's perception of the target object.

An object of the system according to the embodiment is to analyze a comment or a behavior log and to evaluate and improve the acceptability of a service.SOLUTION: A system according to an embodiment includes a comment analysis unit, an action log analysis unit, a learning unit, an evaluation unit, and an improvement proposal unit. The comment analysis unit analyzes the content, text amount, and style of the comment. The action log analysis unit analyzes an action log of a user. The learning unit causes the system to learn based on the analysis results of the comment analysis unit and the action log analysis unit. The evaluation unit sets features of an assumed service and a user and evaluates an acceptability of the service. The improvement proposal unit finds out an improvement point of the service based on the evaluation result.SELECTED DRAWING: Figure 1

The invention relates to the technical field of user comment analysis, and particularly provides a user comment quality double-view reasoning method and system based on state transition learning. The method comprises the following steps: generating an edge set based on a calculated association relationship, and establishing a multi-attribute user comment heterogeneous graph; based on the multi-attribute user comment heterogeneous graph, formulating a user quality dependence rule, and designing a state transition learning algorithm to iteratively infer user quality; based on the user quality, establishing a comment usefulness dependency rule, utilizing a linear function to balance the content usefulness CBH and the content influence IBH, and predicting the comment quality of a general perspective under all products; based on a multi-attribute user comment heterogeneous graph, a designed deep network with a graph aggregation unit and a multi-head self-attention mechanism is utilized to predict the comment quality of a specific view angle under a given user, and the method comprehensively evaluates direct and indirect relationships among the user, a voter and related comments of the user and the voter. And a consumer can be helped to quickly distinguish effective and false information.

The invention relates to the technical field of digital data processing, and discloses a comment analysis system of a commodity target user and an implementation method of the comment analysis system, information files of commodities to be analyzed are received to form a folder of the commodities to be analyzed, so that corresponding comment analysis tasks can be created in the system according to the commodities to be analyzed and a network platform for obtaining a comment source, and the comment analysis task is used for analyzing the commodities to be analyzed. The method comprises the following steps: clearly analyzing a target and a data range, identifying a use scene and a user pain point mentioned in comments, summarizing and obtaining high-frequency common viewpoints, judging an emotional tendency, carrying out statistical classification on the common viewpoints, forming structured data, extracting common characteristics strongly related to a commodity, extracting common points concerned by a user, and finding out a core selling point of the product. And the analysis result can directly serve content creation, a data-driven closed loop is formed, the analysis result is displayed in forms of a chart statistical panel and the like, and users are supported to view commodity core selling points, user feedback distribution, high-frequency keywords and export reports or marketing suggestions.

The invention provides a commodity improvement suggestion generation method and device and a comment analysis system, and the method comprises the steps: obtaining the feature information of a user of a target commodity, obtaining first feature information, and enabling the feature information to comprise one or more of historical behaviors, historical comments and social influence of the user on a consumption platform; forming a first feature vector of each user by the first feature information of each user; inputting each first feature vector into a credibility model to obtain the credibility of each user; filtering the comments of the users whose credibility is lower than a predetermined credibility to obtain referable comments of the target commodity; the reference comments of the target commodity are aggregated to generate the comment abstract of the target commodity, and the improvement suggestion of the target commodity is generated according to the problem feedback content of the comment abstract, so that the problem that the improvement suggestion obtained by comment analysis cannot improve the user satisfaction degree due to distortion of online comments caused by subjective factors of a user in the prior art is solved.

The invention discloses a machine learning model for catering comment analysis, and relates to the field of artificial intelligence, and a construction method of the machine learning model comprises the following steps: S1, obtaining a catering comment training data set; s2, constructing a multi-task neural network model architecture based on the training data set; s3, configuring execution logic of the microscenario gating layer; s4, defining a multi-task joint training objective function; and S5, training the multi-task neural network model by using a training data set to obtain a machine learning model for catering comment analysis. The model can capture internal association among emotion expression, dimension affiliation and scene contexts at the same time, so that the integrity and consistency of analysis results are directly improved, and the integrated modeling mode effectively solves the problems of dimension omission and emotion misjudgment which are likely to happen when composite emotion and multi-dimensional semantics coexist. Therefore, the model has more accurate analysis capability on common contradictions or implicit expressions in catering comments.

The invention belongs to the technical field of comment text analysis, and particularly relates to a customer comment analysis method and system supporting word frequency statistics. According to the method, each comment is associated with the shop exploration task and the check item identifier, so that the comment data directly corresponds to the business index, an enterprise can conveniently and quickly track a problem source, the management efficiency is improved, and semantic-related comment contents are grouped by utilizing word segmentation processing, lexical element feature vectors and semantic clustering; refined semantic analysis of the comment text is realized, semantic confusion possibly brought by simple word frequency statistics is effectively avoided, a low-score check item problem is highlighted in word frequency statistics through performance weighted word frequency calculation, an enterprise can preferentially pay attention to a key business problem, business guidance of an analysis result is enhanced, and the business efficiency is improved. And a user portrait attribute distribution variance self-adaptive down-regulation mechanism is adopted, so that abnormal word frequency caused by deviation of a specific store or a user group is effectively inhibited, and an analysis result is more stable and reliable.

The application relates to a code comment analysis method, computer equipment, a storage medium and a product. The code comment analysis method comprises the following steps: obtaining a target code comment pair, the target code comment pair comprising target code and target comment information; inputting the target code comment pair into a code comment analysis model to obtain an output result; the output result comprising an analysis result for indicating consistency between the target code and the target comment information; wherein the code comment analysis model is obtained by fine-tuning training based on a large language model, the fine-tuning training comprising fine-tuning training based on a code comment consistency detection task and / or fine-tuning training based on a code comment consistency repair task. The consistency between the code and the comment information is judged by using the code comment analysis model which is fine-tuned in a targeted manner, so that the accuracy of the quality evaluation of the code comment pair can be realized, and the software development and maintenance efficiency is improved.

The embodiment of the specification provides a comment analysis page display method, a comment analysis method, a device and a computing device. The comment analysis page display method comprises the following steps: in response to a comment analysis event of target published content, displaying a comment analysis page of the target published content, and displaying comment summary information under at least one analysis dimension on the comment analysis page; wherein the comment summary information under the at least one analysis dimension is generated by a target comment analysis model based on published content information of the target published content, comment content and an interaction weight of the comment content, and the interaction weight of the comment content is used to indicate the influence degree of the comment content on the comment summary information under the at least one analysis dimension. In this way, the comment summary information of the target published content is more efficiently and accurately obtained and displayed on the comment analysis page, efficient acquisition and intuitive display of the comment summary information are realized, user operation is facilitated, and user experience is improved.

The invention relates to an intelligent automobile use scene mining method and system, in particular to an intelligent automobile use scene mining method and system based on a large language model technology. The method comprises the steps of collecting user network comments and constructing a data set; preprocessing data, constructing and training a comment classification model based on BERT, and filtering spam comments; the method comprises the following steps: designing a standardized scene structure system of an intelligent automobile use scene and a prompt template for comment analysis, performing multi-task fine tuning to train a MoLoRAs-QwQ model, and deploying the MoLoRAs-QwQ model to analyze comments; clustering de-duplication and vectorization processing are carried out, and a high-value scene is mined based on an RAG technology; the method can effectively analyze the real vehicle use experience and core demands of the user, and supports the automatic mining of the vehicle use scene which the user really and universally pays attention to.

An object of a system according to an embodiment is to automatically create a cut-out moving image at low cost.SOLUTION: A system includes a comment analysis unit, an emotion analysis unit, a content analysis unit, and a cutout generation unit. The comment analysis unit analyzes the comment field. The emotion analysis unit analyzes the emotion of the distributor based on the data in the comment field analyzed by the comment analysis unit. The detail analysis unit analyzes details of the content on the basis of the emotion data of the distributor analyzed by the emotion analysis unit. The cutout generation unit generates a cutout moving image on the basis of content data of the content analyzed by the content analysis unit.SELECTED DRAWING: Figure 1