189 results about "Opinion analysis" patented technology

The invention relates to a multi-modal public opinion risk early warning system and method based on a dynamic knowledge graph and federal reinforcement learning, and belongs to the technical field of public opinion analysis. The system comprises a data acquisition module, a modal fusion module, a knowledge graph construction module, a comparative learning module, a federal reinforcement learning modeling module and a response output module. The system is based on multi-source heterogeneous data, multi-modal semantic alignment of texts, images, videos and the like is achieved, entity relations and propagation paths are mined through a dynamically updated knowledge graph, collaborative modeling under privacy protection among terminals is achieved by fusing federal reinforcement learning, and then real-time sensing, level early warning and multi-level response strategy recommendation of public opinion risks are achieved. Based on the system, the method has the advantages of high fusion precision, high response speed and strong visual propagation path, and is widely applied to the fields of enterprise crisis management, government affair and public opinion monitoring and public safety.

The invention relates to a network public opinion intelligent prediction system based on dynamic fusion and time sequence analysis, and belongs to the technical field of network public opinion analysis and intelligent prediction. The system comprises an input layer, a feature fusion layer, a time sequence decomposition module, a prediction layer and an abnormity early warning module. The input layer receives data of different time steps and preprocesses the data to generate different feature vectors; the feature fusion layer performs weighted fusion on the feature vectors generated in each time step through a zoom dot product attention mechanism to obtain a fusion feature matrix; the time sequence decomposition module decomposes the fusion feature matrix to obtain a trend component, a season component and a residual component; the prediction layer extracts features of trend, season and residual components, and performs weighted fusion to obtain a public opinion prediction result; and the abnormity early warning module performs early warning based on the abnormity score of the residual component and the error fluctuation and marks the abnormity type. According to the method, dominant laws and implicit association in public opinion evolution can be captured, and high robustness is shown when non-stationary fluctuation caused by emergencies is processed.

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 discloses a user social behavior-based public opinion propagation prediction method and system. The method comprises the following steps of S1, perfecting and dividing data set categories according to data set forms and contents; s2, performing sentiment analysis according to the content of the comments forwarded by the user, and performing preliminary clustering on themes of the tweets forwarded by the user; s3, fusing with user theme features, and performing feature construction; s4, constructing a feature module of the user node, and pruning the whole social network according to the propagation condition; s5, calculating the credibility degree and the susceptibility degree of the user according to the historical forwarding data to construct a mask matrix, and performing similarity evaluation on the propagation network of the specified event; and S6, constructing a propagation prediction result evaluation index. According to the method, the dependency and independence of the user are combined, the user topic features are combined, the user node features with more practical significance are obtained, and compared with traditional public opinion analysis and prediction, the method has higher accuracy.

The invention relates to a big data and deep learning-based public opinion analysis method and apparatus, and a medium. The method comprises the steps of collecting text, image and video multi-modal public opinion data of multiple cities; performing cleaning, duplicate removal, word segmentation and stop word filtering preprocessing operation on the collected public opinion data; respectively extracting text features and image or video features by using a natural language processing technology and a computer vision technology, and fusing the features through a multi-modal fusion algorithm; by adopting a distributed system and a distributed task scheduling algorithm, cross-platform and cross-language efficient data acquisition is realized, resource allocation can be dynamically adjusted according to public opinion popularity of each city, the real-time performance and comprehensiveness of key city data are ensured, and meanwhile, through a method of combining a TF-IDF algorithm, an emotion dictionary and deep learning, the real-time performance of the city is improved. The data quality is effectively improved, and a reliable basis is provided for subsequent analysis.

The invention relates to the technical field of social network analysis and influence prediction, and discloses a large-scale social network influence prediction system and method.The large-scale social network influence prediction method comprises the steps that a multi-language knowledge graph alignment system is constructed, and accurate mapping of cross-language concept nodes is achieved; constructing a culture vector space representation system, and extracting culture features from the social network user behavior data; the resonance intensity calculation between the content and the culture vector is realized, and the resonance intensity of the content in a specific culture environment is quantified; realizing culture gene transmission dynamics simulation, decomposing the content into transmissible culture gene units, and simulating the transmission process of the culture gene units; fusing prediction results to realize accurate influence evaluation; the technical problems that an existing social network influence prediction technology is inaccurate in prediction in a cross-language environment and neglects a culture resonance effect and culture dynamics are solved, and more accurate prediction support is provided for applications such as social media marketing and public opinion analysis.

The invention relates to the technical field of financial risk control, and discloses a financial risk control system based on a block chain and public opinions, and the system comprises a public opinion data collection module which is used for collecting financial-related public opinion data from multiple channels; the public opinion analysis and processing module is used for performing sentiment analysis, keyword extraction and trend prediction on the collected public opinion data; the block chain storage module is used for storing public opinion analysis results and risk related data, and is responsible for the non-tampering and transparency of the data; the intelligent contract execution module is used for dynamically triggering risk control measures according to the public opinion analysis result; the risk assessment and credit scoring module is used for performing dynamic risk assessment on the enterprise and generating a credit score; and the risk early warning and control module is used for generating risk early warning information and automatically executing a risk control strategy. According to the invention, through the distributed account book and the consensus mechanism of the block chain, the system ensures that the whole-process records of the public opinion analysis result, the risk score and the risk control strategy cannot be tampered.

The invention discloses an enterprise exhaustion report intelligent generation method and system based on a large model, and relates to the technical field of artificial intelligence application. The demand understanding module analyzes a complete call demand and a key field in a text input by a user, and presets a template of a complete call report; the intelligent reasoning module constructs execution plans of other modules according to user exhaustion demands and report task characteristics, and formulates report task targets and output standards; the multi-source data scheduling module is in butt joint with a structured database of each administrative department related to an enterprise, and converts a natural language query requirement into an SQL statement capable of directly calling the database; the public opinion analysis module retrieves related news public opinions and performs emotional tendency analysis by using a large model; the intelligent integration module carries out integration according to a preset template of the exhaustion call report to give a comprehensive conclusion; the interactive generation module performs detail adjustment on the exhaustion report based on the real-time feedback of the user to obtain a final exhaustion report; the working efficiency is remarkably improved.

The invention provides an overseas public opinion security situation intelligent perception method and system fused with big data, and relates to the technical field of public opinion analysis, and the method comprises the steps: obtaining overseas public opinion data, extracting multi-dimensional features, employing a bidirectional cross attention mechanism to construct an adaptive weight matrix, generating a fusion feature matrix through multi-head time sequence convolution and a dynamic graph structure attention mechanism, constructing an evaluation index system, determining a weight based on information entropy and an evidence theory, determining an early warning level based on a multi-criterion decision, and generating management and control suggestions and early warning information.

The invention relates to the technical field of public opinion analysis, in particular to an industry public opinion analysis agent construction method and system based on a large language model.The industry public opinion analysis agent construction method based on the large language model.An industry public opinion analysis agent is constructed, industry public opinion information is deeply analyzed, and the industry public opinion analysis efficiency is improved. Compared with other industry public opinion analysis methods, the method has the advantages that the internal semantic association of industry public opinion information can be more effectively and accurately mined, the change trend and hot topics of industry public opinions are timely captured, and the accuracy and comprehensiveness of industry public opinion analysis are improved.

The invention relates to the technical field of data processing, and particularly provides a service pushing method, system and device based on public opinion analysis and a storage medium, and the method comprises the steps: obtaining public opinion data; enterprise entities, events and time are extracted from the public opinion data, and the events are arranged into an event chain according to the time sequence; predicting the operation state of the enterprise entity according to the event chain; and pushing description information of the corresponding service item to the enterprise entity according to a preset corresponding relationship between the operation state and the service item and the operation state of the enterprise entity. According to the invention, the accuracy of service data pushing is greatly improved.

The invention discloses a social network public opinion monitoring method and system based on artificial intelligence. The method comprises the following steps of obtaining public opinion data from a social network platform and performing data preprocessing; hot events are automatically extracted from the public opinion data, and a density peak clustering algorithm is utilized to identify a high-density area and determine the popularity of the hot events. A BERT model is adopted to carry out sentiment classification on a text, context features are extracted in combination with bidirectional LSTM, and sentiment tendency is output through a Softmax classifier. For a specific target event, a multi-head attention mechanism and a convolutional neural network are used for specific target emotion classification. And performing noise reduction and decomposition on the time sequence data, constructing a plurality of basic prediction models, generating a combined prediction model in combination with weighted average, and monitoring and predicting the public opinion development trend in real time. Accurate public opinion analysis and decision support are provided for governments and enterprises, public opinion risks are reduced, social stability is maintained, and social construction is promoted.

The invention discloses a deep learning-based network public opinion monitoring method and system, and relates to the technical field of network public opinion data processing, and the method comprises the steps: carrying out the preprocessing and feature extraction of obtained network public opinion data; after feature extraction, a deep learning model is used for performing sentiment tendency and theme analysis on public opinions; performing public opinion trend prediction by adopting a method of combining an improved particle swarm algorithm and a BP neural network or a deep belief network; and the operation process is integrated into a network public opinion monitoring system to realize monitoring. The deep learning-based network public opinion monitoring method is implemented in a network public opinion monitoring system. According to the method, the problem of limitation of a traditional public opinion analysis method in the aspects of feature extraction, research and judgment accuracy and tracking real-time performance is effectively solved, and intelligent and automatic development of a network public opinion analysis technology is promoted.

The invention discloses a virtual microblog comment generation framework based on a large language model agent. The virtual microblog comment generation framework comprises a user feature modeling module, a behavior modeling module, an information recommendation and random topic module and a user personality evolution module. An intelligent agent is constructed by defining a five-element characteristic system of gender, education degree and the like, and comment generation is controlled by utilizing a prompt template in combination with emotional, standing and irony elements; on the basis of a recommendation system technology and random topic setting, microblog information flow and real-time discussion are simulated; influences of information on users are reflected by means of a personalized evolution mechanism. All the modules operate cooperatively, and large-scale and high-quality Chinese public opinion data can be generated autonomously in real time. The method has the beneficial effects that the data bottleneck is broken through; the data scarcity problem is solved; the data content is updated; improving the model performance: enhancing the generalization ability of the model, and providing diversified training data; a real social scene is simulated; multi-dimensional user simulation is carried out, and a platform function mechanism is simulated; and multi-field research is assisted, and public opinion analysis tasks and service sociology research are supported.

The invention discloses a text information extraction method and system based on a large language model, and belongs to the technical field of natural language processing. Inputting the obtained text information data into the trained text information extraction model, and performing text information processing; wherein the training of the text information extraction model comprises the following steps: constructing a generative and discriminant text information extraction model based on a large language model; constructing a dialogue strategy gradient discrete prompt optimization framework to enhance the prompt stability; and constructing a noise channel matrix, and performing data enhancement and stability enhancement. According to the method, the stability of the model and the accuracy of the extraction result are effectively improved by introducing the judgment component, the balance between generation and copying is optimized by designing the dynamic generation probability calculation module and the extension word list mechanism, the stability and accuracy of text information extraction are effectively improved, and the user experience is improved. And efficient and reliable technical support is provided for scenes such as public opinion analysis and knowledge graph construction.

The embodiment of the invention provides a public opinion analysis method and device, equipment and a storage medium. The public opinion analysis method comprises the steps of obtaining original public opinion data; constructing an initial public opinion graph according to the original public opinion data; the initial public opinion graph comprises a connection relationship between nodes, the nodes correspond to named entities in the original public opinion data, and the connection relationship between the nodes corresponds to an entity relationship between the named entities; generating a multilayer public opinion relationship network according to the initial public opinion graph; generating a public opinion hotspot map according to the initial public opinion map; and obtaining a public opinion analysis result according to the multilayer public opinion relationship network and the public opinion hotspot map. According to the method, the public opinions can be accurately insighted, and the public opinion hotspots are tracked and accurately captured in real time, so that a public opinion analysis result with high timeliness and high accuracy is obtained.

The invention relates to the technical field of public opinion analysis, and discloses a public opinion monitoring method and device, computer equipment and a storage medium, dialogue flow data of a user is obtained through a dialogue flow mode, the user experience is better, the data is analyzed through a trained public opinion big language model, the business requirements of the user are analyzed, and the user experience is improved. The user instruction and the target task corresponding to the user instruction are obtained, for example, public opinion data collection, public opinion data processing, model fine tuning, public opinion report generation and the like, so that the change of user business requirements is quickly adapted, and the flexibility is higher. Finally, the target task is executed through a task flow process mechanism, response and data feedback are conducted on dialogue flow data of the user, the processing efficiency is improved, the public opinion big language model can be made to conduct autonomous iterative learning in the dialogue process, and the intelligent degree of the model is improved.

The invention provides an information processing method and device, a model training method and device, equipment, a medium and a product, and relates to the technical field of computers. The public opinion information processing method comprises the steps that on the basis of a first language model, a crawled original language text is coded to obtain an original language vector, on the basis of a second language model, a target language text is coded to obtain a target language vector, the target language text is generated by translating the original language text, and the first language model is a multi-language model; generating a sentence-level first fusion vector and a word-level second fusion vector based on the original language vector and the target language vector; obtaining corresponding theme features and emotion features based on the first fusion vector; performing entity extraction on the second fusion vector to obtain entity features; and generating a public opinion analysis result based on the subject features, the emotion features and the entity features. According to the technical scheme, the public opinion association capture capability is enhanced through multi-feature joint analysis, and the public opinion analysis efficiency and accuracy can be improved.

The invention discloses a dynamic event propagation prediction system for urban social governance, which is used for accurately classifying and analyzing dynamic event propagation of urban social governance, identifying early propagation signs of events in time and predicting the propagation range, influence and possible evolution paths of the events. The system comprises a public opinion information acquisition subsystem, a public opinion data center, a public opinion data preprocessing subsystem and a public opinion analysis and prediction subsystem, the public opinion information acquisition subsystem is used for performing directional information acquisition on websites in a specified area and a specified field, and the public opinion data preprocessing subsystem is used for performing code recognition, webpage noise removal, text content extraction, Chinese word segmentation and automatic abstract generation on the acquired information and then providing the information to the public opinion analysis and prediction subsystem; and the public opinion analysis and prediction subsystem is used for carrying out keyword frequency analysis, topic detection and tracking and sensitive topic identification on the preprocessed information.

The invention relates to the technical field of natural language processing, in particular to a network public opinion hot topic recognition method, device and equipment, and the network public opinion hot topic recognition method comprises the steps: embedding social media text data through a Chinese pre-training language model to obtain corresponding high-dimensional vector representation; performing dimension reduction on the high-dimensional vector representation by adopting a dimension reduction algorithm based on manifold learning to obtain dimension-reduced text data; and performing density clustering on the text data subjected to dimension reduction, and extracting keywords from a clustering result as recognized topics. According to the method, public opinion topics with high attention and representativeness can be accurately recognized and summarized from massive social media data, the automation level of public opinion hotspot recognition is improved, and more accurate and timely information support is provided for public opinion analysis and decision making.

The invention relates to the technical field of public opinion analysis, in particular to an important emergency public opinion reversal evolution prediction model based on an intelligent algorithm, which comprises a data acquisition module, a data preprocessing module, an emotion analysis module, an evolution path recognition module and a reversal prediction module. Wherein the data acquisition module is used for acquiring public opinion data from multiple data sources in real time; the sentiment analysis module is used for classifying text contents in the public opinions into positive, negative or neutral sentiments; the evolution path identification module is used for constructing a public opinion evolution path network and identifying key nodes and potential rules of public opinion evolution; and the inversion prediction module is used for predicting a public opinion inversion time window and an inversion direction. According to the invention, through combining sentiment analysis, deep learning and public opinion evolution path network technologies, accurate prediction of public opinion inversion of major emergencies is realized, the time window and direction of public opinion inversion can be effectively identified, and scientific decision support is provided for crisis management.

A public opinion analysis visualization platform construction method based on sentiment analysis belongs to the technical field of natural language processing, and specifically comprises the following steps: 1, collecting related corpora and data required by sentiment analysis; 2, preprocessing comment data required by sentiment analysis; and filtering invalid texts, and performing sentence segmentation processing. 3, firstly performing text classification on the processed data, then performing sentiment analysis, judging sentiment tendency in a comment text, and finally performing visual display; and 4, performing public opinion early warning based on an obtained sentiment analysis result and a popularity data analysis result. And 5, generating an independent public opinion evaluation report and a summary report through a related API interface of the local large language model. Through the method, the public opinion response efficiency can be remarkably improved, the risk identification capability is enhanced, the complex language understanding bottleneck is broken through, and meanwhile the manual analysis cost is reduced.

The invention relates to the technical field of artificial intelligence, and particularly provides a public opinion analysis method, system and device and a storage medium, and the method comprises the steps: collecting user evaluation data, and carrying out the preprocessing of the user evaluation data; utilizing a pre-training language model and a topic model to analyze the preprocessed public opinion topic of the user evaluation data; grouping the preprocessed user evaluation data according to public opinion themes to which the user evaluation data belong to obtain a plurality of data groups; and utilizing an emotion analysis model to respectively identify the emotion tendencies of the plurality of data groups to obtain the emotion tendencies corresponding to the plurality of public opinion topics, the emotion analysis model comprising a pre-training language model, a bidirectional long-short term memory network model and a convolutional neural model. According to the method, hierarchical analysis of the user evaluation data is realized, and data support is provided for subsequent decision making.

The invention discloses a potential risk hidden danger identification method and device based on multi-dimensional information and event causal sorting, equipment, a medium and a product, and relates to the field of artificial intelligence. The method comprises the following steps: acquiring multi-modal data from a subscribed public opinion monitoring object, performing feature extraction, and extracting multi-modal information features; performing data structured analysis based on the multi-modal information features, and extracting key attribute structured data of each event; performing clustering analysis on all the events, and dividing all the events into a series of large event sets; performing information deduplication and integration analysis on each small event in the large event set to obtain key attribute structured data of each large event set, performing causal chain sorting on an occurrence sequence and a causal relationship of a series of large event sets to obtain an event causal chain, and performing potential risk hidden danger identification on the event to obtain a hidden danger identification result. The comprehensiveness and timeliness of public opinion monitoring and the accuracy of public opinion analysis can be improved.

The invention relates to the technical field of artificial intelligence engineering, and provides a supply chain management-oriented multi-modal AI large model fusion software platform construction method, which adopts the following technical scheme that the supply chain management-oriented multi-modal AI large model fusion software platform construction method comprises the following steps of: collecting multi-source heterogeneous data in a supply chain, comprising real-time inventory change data, historical demand fluctuation characteristics, emergency semantic information, transportation mode multi-modal information, supplier risk scores and public opinion analysis results, and non-structured documents and numerical indexes of cross-enterprise business processes, a multi-modal AI large model fusing texts, images and time sequence information is constructed based on the collected data, and the multi-modal AI large model is used for analyzing the data. And performing semantic feature extraction, numerical index fusion modeling and risk prediction, outputting based on a multi-modal AI model, dynamically regulating and controlling an inventory water level, adaptively regulating and controlling a purchase plan, intelligently regulating and controlling a logistics scheduling strategy, foresight regulating and controlling a supply chain cooperation strategy, and regulating and controlling an order fulfillment progress in real time.

The invention discloses a news public opinion analysis method based on multi-modal graph representation learning, and belongs to the field of multi-modal deep learning. Constructing a graph structure from the text and image features by adopting a graph topology extraction and enhancement module; a cross-modal multi-scale feature fusion module is adopted to align and integrate features of texts and images in a multi-scale manner, and the cross-modal multi-scale feature fusion module comprises an ASPP and a global attention mechanism module; a final expansion mode feature, a single-mode text mode feature and an image mode feature are obtained through an image topology extraction and enhancement module and a cross-mode multi-scale feature fusion module and are input into a classifier together to obtain respective probabilities, and finally a news public opinion classification result is obtained through weighted fusion. The performance of the DCPNet on a multi-mode data set is superior to that of a traditional single-mode method and other existing multi-mode irony detection methods, and the huge potential of the DCPNet in practical application is proved.

The invention discloses an automatic news event extraction and combination system based on chapter-level text analysis, which is characterized in that text noise is cleaned and paragraphs are divided through a preprocessing module, and strong event candidates are screened by combining multi-modal conjoint analysis of picture titles and paragraph texts and utilizing semantic similarity calculation and named entity recognition technologies; a multi-task learning framework based on a Transform architecture is adopted to cooperatively extract elements such as time, places, figures, events, causes and passes, and the comprehensiveness and accuracy of event element extraction are improved; and the event similarity is calculated through a Bi-Encoder model, and the events are combined and sorted in combination with timestamps, so that an event chain with clear logic is generated. The system breaks through the limitation of existing sentence-level or paragraph-level analysis, remarkably improves the comprehensiveness, consistency and coherence of news event identification through chapter-level global context, data multi-mode combination and intelligent event chain construction, and has wide application value in the fields of news monitoring, public opinion analysis and the like.

The invention discloses a public opinion analysis system based on text hotspots and emotions, and the system comprises a data obtaining module which is used for obtaining text data on a social platform and a corresponding user ID; the data cleaning module is used for cleaning the acquired text data; the topic analysis module is used for performing feature extraction and clustering on the text data; the sentiment analysis module is used for extracting sentiment information and reflecting the sentiment tendency and change of the social platform user; and the comprehensive analysis module is used for comprehensively analyzing the topic display result and the negative emotion new classification result. According to the method, topic analysis and sentiment analysis are included at the same time, and the results of topic analysis and sentiment analysis are further fused; and according to a fusion result of topic analysis and sentiment analysis, performing comprehensive analysis to obtain whether a person performs public opinion guidance or not so as to fully and comprehensively use text data to perform public opinion analysis.

The invention belongs to the technical field of natural language processing and text mining, and discloses an online forum-oriented low-resource topic key topic extraction method, which comprises the following steps of: performing semantic-preserving data enhancement on an original text through a large language model to generate an enhanced document set; utilizing a pre-training language model to extract context-aware semantic representation of the document; constructing a learnable topic embedding matrix, and calculating and generating topic distribution; designing a semantic perception contrast learning framework, and optimizing theme diversity by adopting a dynamic negative sample screening strategy; and meanwhile, priori alignment loss is used for ensuring theme consistency. According to the invention, an LLM enhanced data expansion mechanism and a lightweight theme coding architecture are creatively fused, and through dual optimization of contrast learning regularization and prior distribution matching, three technical problems of data sparsity, model over-fitting and noise sensitivity in a low-resource scene are effectively solved; and an efficient and reliable theme modeling solution is provided for social media public opinion analysis.