107 results about "Sentiment score" patented technology

The invention relates to the technical field of social network analysis and generative artificial intelligence, and discloses a multi-agent-based complex social behavior simulation and public opinion deduction method, which comprises the following steps of: dividing social groups, and dividing users in a social network into key opinion leader communities and common user communities; respectively constructing a key opinion leader agent and a common user agent which are driven by the large language model; constructing a dynamic weighted directed network, taking each key opinion leader agent model as a node, calculating a network node neighborhood through opinion index and node influence weighting, calculating an emotion score based on a text emotion index, dynamically adjusting the network node neighborhood based on the emotion score, and performing loop iteration for several times to complete deduction and simulation of network public opinions; the problem that public opinion propagation modeling is difficult under the cross-domain dynamic background is solved, the difference of public opinion individuals and the complex social interaction relation can be simulated more accurately, and the method is feasible.

According to the product public opinion analysis method and device, the storage medium and the computer equipment provided by the invention, the multi-modal features in the public opinion data are extracted and feature fusion is carried out to obtain the fused feature data. In this way, information in different forms can be effectively integrated, and the public opinion situation can be analyzed more comprehensively and accurately. And then extracting an entity relationship in the fused feature data to update a preset public opinion knowledge graph. And performing sentiment analysis on the fused feature data in three dimensions of text, video and voice by using a public opinion knowledge graph to obtain a public opinion sentiment score. Therefore, the emotional tendency of the public opinion information can be captured more comprehensively, and the accuracy of public opinion analysis is improved. Meanwhile, an early warning mechanism is triggered when a preset condition is met. And finally, reasoning and plan matching are performed in the public opinion knowledge graph based on the generated public opinion event features so as to determine a public relation plan. According to the process, historical experience and reasoning ability in the knowledge graph can be fully utilized, and the timeliness and accuracy of public opinion management are improved.

The invention belongs to the field of data processing, and discloses a social media comprehensive public opinion analysis method and related device.The method comprises the steps that feature vectors of all cross-modal public opinion data are obtained through a pre-trained cross-modal large model, and public opinion events are clustered through an unsupervised clustering method according to the feature vectors; utilizing a pre-training sentiment analysis model and a pre-training topic analysis model to respectively obtain sentiment scores and topic sensitivity scores of the public opinion events in the public opinion event clusters; according to the emotion score and the topic sensitivity score of each public opinion event in each public opinion event cluster, respectively obtaining an emotion score index score and a topic sensitivity index score of each public opinion event cluster, and obtaining a comprehensive public opinion analysis score of each public opinion event cluster in combination with the exposure index score, the interactivity index score and the propagation intensity index score; and obtaining a public opinion evaluation result of each public opinion event cluster according to the comprehensive public opinion analysis score of each public opinion event cluster, thereby realizing comprehensive perception and accurate study and judgment of the public opinion situation.

The invention relates to a method for dynamically constructing a service quality evaluation system fused with a large language model, which comprises the following steps of: extracting themes, keywords and representative documents from a user evaluation text data set, constructing cue words and inputting the cue words into the large language model to generate candidate primary service dimensions divided by five service dimensions of an SERVQUAL model; the service quality candidate index item list is composed of candidate second-level topics and candidate third-level index items; constructing a multi-channel heterogeneous hypergraph set fusing sliding window co-occurrence and an external knowledge graph; graph structure information is extracted, joint prediction of emotion labels and emotion scores of user evaluation texts is carried out, and topic-level multi-class input factor spaces are constructed to be used for calculating final fusion weights; and performing comprehensive sorting and dynamic screening on the candidate three-level index items to form a multi-level service quality evaluation system index scale. According to the method, accurate modeling of the user perception quality is realized, and the emotional discrimination of the indexes of the service quality evaluation system can be improved.

Automated computer systems and methods to determine a sentiment of information in digital information or content are disclosed. One aspect includes deriving, by a processor, the digital information from a source; generating, by the processor, a domain-specific machine learning sentiment score, based on the digital information, by one model of at least two machine learning models; autonomously mapping, by the processor, a non-domain specific knowledge graph of associations between elements in a set of digital contextual information; receiving, by the processor, sentiment graphs, each sentiment graph defining a sentiment; generating, by the processor, a graph sentiment score based on the non-domain specific knowledge graph and the sentiment graphs; generating, by the processor, a final sentiment score based on the graph sentiment score and the domain-specific machine learning sentiment score; and determining the sentiment of the information in the digital information or content via the final sentiment score.

The invention discloses a hotel check-in information management method and system based on a database, and belongs to the technical field of information management. Nodes and edges are defined based on the extracted data, node embedding is generated through a random walk method, a customer classification model is constructed, and classification labels of customers are generated; generating a room distribution result by constructing a linear programming model based on the classified customers in combination with the real-time room related information of the hotel; through short-time Fourier transform and BERT model extraction emotion features and text embedding, constructing an emotion analysis model and a demand recognition model, and generating an emotion score and a demand recognition result; and based on the customer classification, the room allocation result, the emotion analysis result and the demand identification result, using a logic rule to generate a personalized service recommendation list. An intelligent management mode from efficient data extraction, accurate customer classification and dynamic room allocation to real-time service response and recommendation is realized.

A method for managing an organization's performance includes: receiving an adjacency matrix (AM) from a document attention module; determining, by employing a first model, a first k-hop value for a first transcript and a second k-hop value for a second transcript based on the AM; analyzing the first k-hop value, the second k-hop value, and the AM; generating, by employing a second model, a second graph representation based on relationships in the AM; transforming the second graph representation into embedding vectors (EVs); analyzing, by employing a language model, the EVs to extract features; analyzing the features to generate a first sentiment score (SS) for a first sentence of the first transcript and a second SS for a second sentence of the first transcript; and classifying the first sentence as a first negative sentence and the second sentence as a second negative sentence.

The invention relates to the technical field of computer technologies, and discloses an old people social network analysis and recommendation system based on a graph neural network, which comprises a data acquisition module, a graph construction module, a graph neural network model module, a social network analysis module, a personalized recommendation module and a user interface module, the data acquisition module is used for acquiring and cleaning social related data of old people; and the graph construction module is used for constructing static and dynamic social network graphs. According to the old people social network analysis and recommendation system based on the graph neural network, a dynamic social network graph is constructed through the graph construction module, sliding time window updating is adopted, dynamic changes of the social relation of old people along with time can be captured, meanwhile, edge weight calculation is combined with the interaction frequency and the interaction depth, and the recommendation efficiency is improved. The interaction depth fuses semantic similarity and emotion scores, the interaction depth is accurately quantified, and a social network analysis module introduces a time decay factor prediction relation.

The embodiment of the invention provides a marketing verbal skill generation method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology and artificial intelligence. The method comprises the following steps: acquiring multi-modal data; performing cross-modal feature fusion on the voice data and the text data to generate a joint feature vector; based on the joint feature vector, through an attention mechanism, a multi-dimensional emotion evaluation result is generated, and the multi-dimensional emotion evaluation result comprises an evaluation result of at least one preset emotion dimension; and according to the multi-dimensional emotion evaluation result, generating and pushing a marketing verbal skill corresponding to the multi-dimensional emotion evaluation result. According to the method provided by the invention, through joint modeling of voice and text features and in combination with an attention mechanism, a key emotion region is focused, so that the accuracy of emotion scoring is remarkably improved, and the attitude of a customer can be reflected more truly; according to the emotion evaluation result, the optimization suggestion is generated, the generated verbal skill accurately matches the demand of the customer, the response efficiency of the marketing verbal skill is improved, and the customer emotion improvement rate is greatly improved.

The invention discloses a user comment authenticity verification method and device and a medium, and belongs to the technical field of data analysis. The method comprises the steps of obtaining historical user comment data of an e-commerce platform; performing standardization processing on the historical user comment data to output standardized comment data; performing semantic coding and emotion tendency analysis on the standardized comment data based on a language model to generate a high-dimensional semantic vector and an emotion score; obtaining and calculating user behavior characteristic data based on user historical behavior data; constructing a multi-input deep learning model, and training the multi-input deep learning model through a preset annotation data set to determine a comment classification model; and performing real-time analysis on new comments of the e-commerce platform based on the comment classification model to output a comment authenticity score, and comparing the comment authenticity score with a preset threshold to determine credible comments, suspected false comments and false comments. According to the method, the technical effect of detecting the authenticity of the comments of the user is achieved to a certain extent.

The invention discloses a Chinese culture element overseas propagation multi-modal prediction model construction method, which comprises the steps of S1, obtaining a short video and short video comments, performing data cleaning on the short video comments, performing aspect-level sentiment analysis on the comments, and obtaining main evaluation dimensions, dimension weights and sentiment scores; s2, dividing the short video into multiple frames, extracting corresponding Chinese culture elements and explanations thereof in each frame of picture, synthesizing the culture elements of each picture and extracting second-level Chinese culture elements; s3, linking the video framing image and the evaluation dimensions, mapping the framing image and the evaluation dimensions to the same semantic space, and obtaining dimension scores of the video framing image on each evaluation dimension; s4, acquiring a propagation effect score of each Chinese culture element to obtain an indirect score of the propagation effect of the Chinese culture element; s5, obtaining a direct score of the Chinese culture element propagation effect; s6, defining a score fusion function, fusing the direct score and the indirect score of the Chinese culture element propagation effect to obtain a final score of the Chinese culture element propagation effect, endowing a final score of a second-level Chinese culture element propagation effect, and constructing a data set; and S7, training the base multi-modal large model to obtain a Chinese culture element overseas propagation multi-modal prediction model.

The invention discloses an intelligent customer service method and system based on multi-modal dynamic fusion, and the method comprises the steps: obtaining a user service demand and a behavior log on a service terminal; performing feature extraction and dynamic fusion on the user service demand and the behavior log; inputting the fusion features into a multi-task learning model to obtain intention categories and emotion scores; and generating a service action through reinforcement learning based on the intention category and the emotion score. Through multi-modal dynamic fusion and an active service mechanism, the intention of the user can be more accurately understood, and the corresponding service action is provided at the first time, so that the dialogue turns are reduced, and the service efficiency is improved.

The invention discloses a data behavior prevention and control and anomaly detection system based on big data, and relates to the technical field of data security. Comprising a data acquisition module used for acquiring user behavior data; the feature construction module is used for constructing emotion features, space-time trajectory features and abnormal data features according to the preprocessed user behavior data; the calculation module is used for calculating to obtain a comprehensive score according to the emotion features, the spatial-temporal trajectory features and the abnormal data features; the threshold judgment module is used for respectively carrying out threshold judgment on the comprehensive emotion score, the comprehensive space-time score and the comprehensive generated data score, and generating a first abnormal signal, a second abnormal signal and a third abnormal signal when a threshold condition is exceeded; and the model construction module is used for constructing an anomaly detection model and generating a fourth anomaly signal when an anomaly is detected. Through multi-dimensional feature fusion, real-time anomaly detection and a deep learning technology based on big data, the accuracy of abnormal behavior recognition is improved, and the security and reliability of the system are enhanced.

Artificial Intelligence driven system for managing deployment actions based on sentiment analysis includes performing operations including obtaining an assigned feature name for an application feature of a target application, selecting, using respective vector encodings of an application feature name that include the assigned feature name, and filtering a collection of feedback using a set of application feature names of an application feature to obtain a filtered set of feedback. The operations further include generating a sentiment request prompt requesting a sentiment score and comprising the filtered set of feedback and the set of application feature names, and processing, by a large language model (LLM), the filtered set of feedback with the set of application feature names to generate the sentiment score for the application feature. The operations further include selecting a recommended action based on the sentiment score and updating the target application according to the recommended action.

The invention discloses a social text standing recognition method and system based on claim generation and consistency analysis, and relates to the field of natural language processing, and the method comprises the steps: obtaining an original user tweet text of a user, and carrying out the preprocessing of the original user tweet text, and obtaining a preprocessed user tweet text; constructing a text pair according to the preprocessed user tweet text; inputting the text pair into a pre-trained semantic consistency recognition model to obtain a consistency recognition result; wherein the consistency identification result comprises a semantic consistency score and a semantic site classification result; performing sentiment analysis on the preprocessed user tweet text based on a preset sentiment dictionary, and calculating a sentiment tendency score; performing weighted fusion on the semantic consistency score and the emotion score to obtain a comprehensive standing tendency score; and according to the comprehensive standing tendency score and the semantic standing classification result, obtaining the standing of the user. According to the method, the stability and the reliability of social text standing recognition in a scene with strong emotion and changeable language styles are improved.

Systems and methods herein provide for establishing a subjective viewpoint in text. In one embodiment, a method includes identifying intents and metrics in each of a plurality of texts, calculating a sentiment score for each text based on the identified intents and metrics of each text, and calculating a disfluency score for each text to weight the sentiment score of each text. The method also includes training the machine learning model with the texts, and processing a subsequent text through the trained machine learning model to determine a sentiment score of the subsequent text.

A computer-implemented method comprising: obtaining real-time text data relating to a matter, the text data comprising a plurality of portions of information; performing a categorisation process, wherein the categorisation process is configured to run a plurality of threads in parallel, wherein each thread of the plurality of threads acts on one portion of information at a time, wherein each thread performs the following steps: (i) obtaining a sentiment score based on the portion of information using a Sentiment Analysis machine learning, ML, model; (ii) assigning a category to the matter based on the sentiment score using a classification ML model trained on historical data; and (iii) updating a live category based on the category assigned to the matter; and finally, outputting the live category to a user in real-time.

This application discloses a method and apparatus for quantifying the quality of rural hotels, as well as a method for processing rural hotel information, belonging to the field of data processing and information technology. The method includes: screening and confirming rural hotels from accommodation facilities within a target area according to preset identification rules to obtain a rural hotel directory; collecting online user review text data for each rural hotel; extracting high-frequency feature words based on the online user review text data, performing semantic analysis, and constructing a quality evaluation index system; mapping each review text to the corresponding index in the quality evaluation index system, and obtaining the global weight of each level of index by statistically analyzing the number of review texts corresponding to the first-level and second-level indicators; performing sentiment analysis on each review text and calculating the sentiment score of each level of index; and calculating the quality quantification score of each rural hotel based on the sentiment score corresponding to each level of index and its corresponding global weight. The embodiments of this application can improve the accuracy of rural hotel quality quantification.

A method and system for investing in securities, equities, and funds is currently provided. The method comprises a customer selecting at least one security, equity, or fund from their current holdings, comparing their selection with other available securities, equities, and funds in a database and calculating a sentiment score for each of the compared alternatives. The sentiment score is a calculated likeness between, or an inverse distance from, the customer's selection and the compared securities, equities, or funds. The compared securities, equities, or funds are ranked by their sentiment scores and an expense ratio, a 12b-1 fee, and a front load fee associated with at least an upper ranked portion of the compared securities, equities, or funds is reported.

A method for managing an organization's performance includes: receiving an adjacency matrix (AM) from a document attention module; determining, by employing a first model, a first k-hop value for a first transcript and a second k-hop value for a second transcript based on the AM; analyzing the first k-hop value, the second k-hop value, and the AM; generating, by employing a second model, a second graph representation based on relationships in the AM; transforming the second graph representation into embedding vectors (EVs); analyzing, by employing a language model, the EVs to extract features; analyzing the features to generate a first sentiment score (SS) for a first sentence of the first transcript and a second SS for a second sentence of the first transcript; and classifying the first sentence as a first negative sentence and the second sentence as a second negative sentence.

A method and system for evaluating contract-worthiness of performing artists is disclosed. The method includes receiving consolidated data corresponding to each of a set of performing artists. The method further includes processing the consolidated data by classifying a set of topics in the text data into a plurality of categories through a ML classification model, determining user engagement behavior segments based on sentiment scores associated with the text data, and determining a plurality KPIs based on the plurality of metrics, the plurality of categories, and the user engagement behavior segments. Further, the method includes calculating a contract worthiness score for the set of performing artists based on the plurality of KPIs using a trained ML contract worthiness scoring model and evaluating one or more of the set of performing artists for their contract worthiness based on the contract worthiness score and a threshold contract worthiness score.

The application discloses an intelligent customer service interaction method and system, and relates to the technical field of intelligent customer service interaction.The method comprises the following steps: obtaining a user request; calculating a comprehensive sentiment score through multi-modal sentiment analysis based on the user request; classifying the user's emotional state according to the comprehensive sentiment score, initializing the dialogue context, and constructing a dialogue context graph according to the classification result; dynamically adjusting the response mode of the intelligent customer service according to the emotional state and the user's question in the dialogue context graph, and collecting the feedback information of the user; and dynamically adjusting the dialogue strategy based on the feedback information of the user and the dialogue context.The application can comprehensively capture the emotional state of the user from multiple angles such as text, voice and facial expression through multi-modal sentiment analysis, improves the accuracy and robustness of emotion recognition, provides a reliable basis for subsequent dialogue management and response mode, and improves the experience satisfaction and service quality of the user.

The invention discloses a social media data fused urban inefficient land intelligent identification method, which comprises the following steps of: realizing multi-source data space fusion by acquiring a social media geographic marking text, an urban updating policy file, a remote sensing image and basic geographic data; a natural language processing and topic modeling method is used for extracting keywords from the city update text, a topic-feature word mapping table is constructed, a RoBERTa-l arge model and a semantic rule are combined to calculate a text emotion score, and a land use negative emotion map is generated; and constructing a multi-dimensional evaluation system including indexes such as plot ratio and road network density, calculating a composite index by adopting an AMP algorithm, dynamically allocating weights through an entropy weight method, and judging the low-efficiency land. And a white list area is introduced for verification and parameter iterative optimization, and finally, an identification result is visually displayed in WebG IS and AR platforms, so that the accuracy and decision support capability of urban low-efficiency land identification are improved.

The application discloses a kind of text writing revision auxiliary method and system, it is related to artificial intelligence technical field, method includes: obtaining the current text description of user, extract entity text and carry out semantic sorting, generate entity text sequence;The specification score, sentiment score and writing object level of each entity text are identified by word vector model, generate first projection point;With each entity text as center text, the projection point of adjacent text in window is fused, to obtain second projection point, build current scene polyhedron;Each first projection point of entity text sequence and current scene polyhedron are input to writing detection model, model is encoded by multi-head self-attention and 3D grid convolution operation, generate text and scene vector and weighted fusion, decode output error prediction and position push to user.By implementing the application, the multidimensional characteristics of the scene are retained, thereby solving the problem that a single scene tag cannot reflect the real scene with multiple elements interwoven in the prior art.

The invention discloses a method, device and equipment for adjusting background light of a terminal with a screen and a storage medium, and relates to the technical field of intelligent hardware. The method comprises the following steps: acquiring figure video data, text data and audio data in a video; inputting the character video data, the text data and the audio data into a sentiment analysis model, extracting modal features and inter-modal features of the character video data, the text data and the audio data through the sentiment analysis model, and calculating decoupling feature input according to a relation among the modal features, the inter-modal features and the decoupling features; outputting a target sentiment score predicted value according to a mapping relationship between the decoupling features and the sentiment score predicted value; and adjusting the background light according to the target emotion score predicted value. According to the embodiment of the invention, the real-time adaptive adjustment of the background light is realized, and the background light interaction adjustment capability is improved.

The invention discloses a book recommendation method fusing existing user borrowing behaviors of a library and Internet resources. According to the method, book quality evaluation indexes comprehensively consider the following four factors: influence weights of readers on book types, return timeliness, borrowing group breadth and Hubamp; an Authority method is used for comprehensively measuring the quality of the books; the implementation process of the recommended purchase method comprises the following steps: firstly, collecting all recommended purchase requests to be processed from a library database, and constructing an initial recommended purchase list; thirdly, screening first three books which are most similar to the books in the list through cosine similarity, and extracting borrowing data of the first three books; thirdly, combining the borrowing records with comments of read users on the social media, and performing multi-modal sentiment analysis to obtain sentiment scores; then, integrating the emotion score with the book special information by using a random forest model to form a comprehensive score; and finally, optimizing recommended purchase list sorting according to the comprehensive score, and determining a final book purchase priority.

The invention discloses an interactive audience participation feedback method based on sentiment analysis and behavior prediction. User behavior data and text sentiment information are collected in real time through an audience terminal, behavior analysis and sentiment recognition are executed in parallel on a cloud server, and behavior tags and multi-level sentiment scores are generated; in combination with the historical portrait of the user, personalized interactive feedback content including an expression package, a knowledge point card, a microphone connection invitation or a voting entry is dynamically generated through a weighted scoring mechanism, and the pushing frequency is controlled to be not more than once every three minutes to avoid interference; meanwhile, when the individual continuously carries out negative feedback or the overall negative emotion ratio of the live broadcast room exceeds a threshold value, an instruction is automatically sent to the live broadcast stream pushing end, and real-time content adjustment is achieved. The method effectively solves the problems that traditional live broadcast interaction lags behind, is single in form and lacks emotional response, remarkably improves the audience immersion, participation and platform retention rate, and is suitable for various live broadcast scenes such as education, entertainment and e-commerce.

The application relates to the technical field of artificial intelligence customer service, and provides an intelligent customer life cycle management AiCRM method and system, which are used for solving the problems of low customer service accuracy and poor response timeliness in the prior art. In the application, the method comprises the following steps: in the interaction process between a target customer and an enterprise, customer voice, expression and text data are acquired, multi-modal sentiment analysis is performed, and a customer sentiment feature set is generated; a customer sentiment evolution graph is dynamically updated based on the customer sentiment feature set, accurate customer sentiment scores are generated by combining historical data and real-time interaction information and using a sentiment calculation model; and when the score is lower than a preset baseline, a personalized service strategy adjustment scheme matched with the current emotional state of the customer and the historical change trend is automatically generated by the system. The application improves the customer service accuracy and response timeliness.

A method and system for investing in securities, equities, and funds is currently provided. The method comprises a client selecting at least one security, equity, or fund from their current holdings, comparing their selection with other available securities, equities, and funds in a database and calculating a sentiment score for each of the compared alternatives. The sentiment score is a calculated likeness between, or an inverse distance from, the client's selection and the compared securities, equities, or funds. The compared securities, equities, or funds are ranked by their sentiment scores and an expense ratio, a 12b-1 fee, and a front load fee associated with at least an upper ranked portion of the compared securities, equities, or funds is reported.

The invention provides a label extraction method and device in the field of artificial intelligence, which are used for analyzing user comments and determining characteristic comments in the user comments in combination with emotion scores of the user comments and comprehensive labels. The method comprises the following steps: classifying user comments according to a sentiment classification model to obtain sentiment categories of the user comments; according to sentiment words in the user comments and related vocabularies of the sentiment words, calculating to obtain sentiment scores of the user comments; according to the emotion category, analyzing the user comments to obtain a comprehensive label of the user comments, the comprehensive label being used for summarizing feature information of the user comments; and obtaining characteristic comments in the user comments according to the emotion scores and the comprehensive labels.