115 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.

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 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 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.

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 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 relates to the technical field, and discloses a development method of an intelligent public opinion analysis and prediction system based on deep learning. Comprising the following steps: a data acquisition step, a data preprocessing step, a feature extraction step, a public opinion analysis model construction step, a public opinion prediction model construction step, a model training and optimization step, a public opinion monitoring and real-time analysis step and a public opinion prediction and early warning step. According to the development method of the intelligent public opinion analysis and prediction system based on deep learning and the related implementation modes, through a series of technical means and optimization measures, the quality and value of public opinion data can be effectively improved, the accuracy and comprehensiveness of public opinion analysis are enhanced, the precision and reliability of public opinion prediction are improved, and the development efficiency is improved. The model training and updating efficiency is optimized, and timely and effective public opinion early warning is realized.

The invention provides an automatic research, judgment and coping method and device for a network public opinion event based on a large model agent. The method comprises the steps of obtaining public opinion data of the public opinion event at a current time node; performing situation awareness analysis on the public opinion data of the current time node to obtain situation data of the current time node; based on the situation data of the current time node, the basic information of the public opinion event and the element information of a plurality of historical key time nodes, adopting a pre-trained public opinion coping large model to generate a coping measure of the current time node; and automatically coping with the public opinion event based on the coping measure of the current time node. According to the public opinion research and judgment coping method and device, the coping measures of the public opinion event can be automatically generated and coping of the public opinion event can be automatically carried out through the public opinion large model, manual participation is not needed in the whole public opinion research and judgment coping process, automation is comprehensively achieved through the large model agent, and therefore the public opinion research and judgment coping accuracy and response speed are greatly improved.

The invention relates to the technical field of semantic recognition, in particular to a cross-border commodity multi-language comment semantic analysis method, which comprises the following steps of: acquiring a cross-border commodity multi-language comment text, constructing nodes and directed edges according to part-of-speech tagging and dependency relationship rules, generating a text syntax dependency directed graph, and generating a text syntax dependency directed graph; and traversing the text syntax dependency directed graph, calculating a node shortest dependency path between nodes, and generating a pruned syntax subtree. According to the method, the emotion consistency coefficient index is calculated and compared with the logic smoothness threshold value, so that the emotion stability and logic self-consistency in the comments can be quantitatively evaluated. The mechanism not only completes judgment of single-dimension emotional tendency, but also introduces verification of text logic quality, and effectively recognizes and filters out semantic contradictions and false comments or low-quality texts with obvious splicing traces, so that a cross-border commodity semantic analysis set which contains core semantic information and has high credibility is generated; and the precision of public opinion analysis is improved.

The invention provides a vehicle public opinion intelligent analysis method and system based on the Internet of Vehicles, a product and a vehicle, and relates to the technical field of vehicle public opinion analysis. The emotion contribution degree of each data source is dynamically evaluated and weighted by using a cross-modal attention mechanism, so that the one-sidedness of single-modal analysis is effectively overcome, and the accuracy and robustness of public opinion emotion judgment are improved; further deeply mining public opinion propagation information by means of a space-time diagram neural network, constructing a propagation path model, generating an event-emotion-propagation force three-dimensional thermodynamic diagram, and realizing visual insight and trend prediction of a public opinion evolution situation; on the basis, a local and external collaborative grading emergency response mechanism is constructed, user anxiety is relieved, and diffusion of bad public opinions is effectively restrained.

The invention provides a credit debt price fluctuation prediction method and prediction device based on public opinion analysis, and the method comprises the steps: obtaining first public opinion data and second public opinion data, determining a first event tag and a second event tag based on the first public opinion data and the second public opinion data, and determining a risk conduction coefficient according to correlation; integrating the second public opinion data and the first public opinion data according to the risk conduction coefficient to obtain a data wide table; processing the data wide table through an APRIORI algorithm and an LSTM algorithm to obtain an event combination set and an event chain; and predicting a price transaction probability through a logistic regression model or predicting a first preset risk event occurrence probability through an MLP model based on the event combination set and the event chain, and generating target alarm information under the condition that any probability exceeds a limit. The method solves the problem that in the prior art, public opinion data mining is insufficient in a credit debt evaluation method, so that the accuracy of an evaluation result is low, and enterprise financial loss is caused.

The invention discloses a natural language processing-based risk public opinion analysis method, which comprises the following steps of: capturing a public opinion text in real time, and desensitizing a user identity field by adopting a Hash pseudo identifier to obtain an original text stream; constructing a double-classification weak supervision framework, calculating a risk confidence coefficient, and when the risk confidence coefficient is greater than or equal to a first threshold value, writing a corresponding text into a labeling pool to form a risk corpus; mapping the risk corpus into a dynamic heterogeneous risk knowledge graph according to an entity-relationship-time slice meta-path rule; applying a multi-head space-time diffusion Transform to a sub-graph intersecting with the current window in the graph to obtain a risk propagation vector of an event node; and inputting the risk propagation vector and the historical baseline vector into a causal comparative analysis model, outputting a risk early warning index, and when the early warning index is greater than or equal to a second threshold value, pushing a risk alarm including an event path to a background management and control center. According to the invention, the automation degree, accuracy and reliability of public opinion risk monitoring and early warning are effectively improved.

The invention discloses a public opinion data structuring method based on a Re-Act framework, and relates to the technical field of public opinion data processing and intelligent structuralization, and the method comprises the steps: firstly constructing a Re-Act public opinion structuring system comprising a thinking module, an action module and an observation module, then collecting data through multiple rounds of closed-loop iteration until a full link is covered, and finally obtaining the data. And finally, processing based on the full-link data through a structured tool, outputting standardized data and a relation graph, and terminating. According to the scheme, public opinion cause, response, action, interaction and ending full-link data are dynamically complemented through multiple rounds of closed loops of thinking, action and observation under the Re-Act framework, structured quality is optimized by combining multi-module processing, standardized data and a relation graph are output, public opinion dynamic characteristics are adapted, a complete and accurate support is provided for deep analysis and decision making, and the public opinion analysis and decision making efficiency is improved. And the limitation that the link is easy to break and the adaptability is poor in the traditional method is overcome.

The application relates to a construction method for obtaining a viewpoint evaluation object model, and belongs to the field of public opinion analysis. An event abstract text generation model is established, which is used for inputting information text containing event content to obtain event abstract text; a sentence annotation model is established, which is used for inputting viewpoint text for evaluating the event and the event abstract text to obtain a viewpoint with the event abstract text as semantic guidance; wherein the viewpoint is a sentence sequence extracted from the viewpoint text; a viewpoint evaluation object extraction model is established, which is used for inputting the viewpoint and the event abstract text to obtain an evaluation object of the viewpoint; wherein the evaluation object is a word sequence extracted from the event abstract text. The application can accurately obtain a viewpoint with the event as an evaluation object, and improves the analysis performance.

The invention belongs to the technical field of network public opinion prediction, and particularly relates to a network public opinion analysis and prediction method and system based on a space-time diagram neural network. The method comprises the following steps: acquiring network public opinion data, extracting public opinion texts, release time and forwarding, replying and reference relationships, and constructing a bilateral space-time heterogeneous relationship graph containing semantic association edges in time layers and propagation causal edges between the time layers at the same time; further extracting a text representation feature, a network position feature, a propagation statistical feature and a cross-layer propagation feature to form a node input vector, and obtaining a node embedding representation through a space-time diagram neural network; on this basis, a ternary trend state vector composed of propagation activity, propagation attenuation and path offset is constructed, trend classification, propagation direction clustering, key node identification and hot event labeling are completed, and a double-deviation feedback quantity is utilized to carry out iterative correction on an edge weight and a node coding parameter. According to the scheme, structured analysis and trend prediction of the network public opinion propagation process can be realized.

The invention relates to an electric power public opinion data driven work deployment adaptive optimization method and system, and the method comprises the following steps: S1, obtaining multi-source data related to electric power public opinions, filtering noise data, forming a standardized data set, and constructing a unified public opinion data pool; s2, constructing a public opinion knowledge graph, and obtaining a comprehensive public opinion risk index; s3, constructing a multi-target service deployment adaptive optimization model; s4, according to the multi-target service deployment adaptive optimization model, obtaining an optimal work deployment scheme based on a scheme solving and generating method of an adaptive optimization algorithm; and S5, according to the optimal work deployment scheme, pushing the optimal work deployment scheme to a decision maker through a visual billboard, tracking the whole execution process of the scheme, and feeding back an execution result and newly added public opinion data to the step S1. Through real-time public opinion analysis, high-risk events are automatically identified before event expansion, active intervention is realized, and a traditional passive complaint waiting mode is changed.

The invention discloses an information propagation scale prediction method based on multi-feature fusion. The method comprises the following steps: firstly, preprocessing collected information propagation link data and constructing an attention network and a forwarding network; then, extracting multi-dimensional features from three dimensions of a space structure, a time evolution process and a text attribute of information diffusion; and finally, fusing the multi-dimensional features, and inputting the fused multi-dimensional features into a machine learning prediction model for training and prediction so as to realize automatic and accurate prediction of the propagation scale of the information on the social network. According to the method, through multi-feature fusion, the problems of incomplete feature mining and poor prediction precision in the prior art are solved, the information propagation process can be flexibly and effectively quantified, the prediction accuracy and interpretability are remarkably improved, and the method can be widely applied to the fields of network public opinion analysis, hotspot discovery and the like.

The invention provides a consumption public opinion information analysis method, system and device and a medium, and belongs to the technical field of consumption information processing. A consumption information set is obtained, consumption information needing to be analyzed is matched with consumption identifiers in a consumption information library, and whether the consumption identifiers matched with the consumption information needing to be analyzed exist or not is judged; when the consumption identifier matched with the consumption information needing to be analyzed exists, consumption analysis corresponding to the consumption identifier is executed; if the current consumption process is the consumption process of the consumer and the virtual shop assistant, the consumption information needing to be analyzed is input into the consumption information consumption public opinion analysis model; and executing corresponding consumption analysis including information query consumption analysis, consumption process adjustment consumption analysis or guide consumption analysis based on an identification result of the consumption information consumption public opinion analysis model. According to the method, consumption public opinions are fed back in real time, enterprises can be helped to adjust service strategies in time, differentiated processing of virtual and real person scenes gives consideration to service efficiency and personalized requirements, and consumer satisfaction is improved.

The invention relates to a group speech viewpoint tracking method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring comment data about a target object of each user in a group at a sampling time point and character attribute information of each user from a social platform; performing part-of-speech tagging on the comment data, and constructing and analyzing a dependency syntax tree for a part-of-speech tagging result to obtain viewpoint features; performing weighted summation according to the viewpoint polarity score and the polarity weight value corresponding to each viewpoint feature to obtain a personal speech viewpoint polarity score, and determining a personal speech viewpoint according to the personal speech viewpoint polarity score; calculating a viewpoint weight score according to the personal speech viewpoint polarity score of each user and the user weight value, and determining a group speech viewpoint according to the viewpoint weight score; and the group speech viewpoints corresponding to different sampling time points are connected in series according to a time sequence to obtain group speech viewpoint change veins. By adopting the method, the opinion opinions of the group can be dynamically tracked, and efficient and accurate support is provided for public opinion analysis.

The invention discloses a public opinion analysis method and device, equipment and a medium. The method comprises the steps of obtaining historical exchange rate data and historical news data of a target field; based on the historical exchange rate data, screening out target news data from the historical news data; labeling the target news data to obtain labeled data including an emotion label, an importance label and a category label; a plurality of prediction models are trained based on the annotation data, and the prediction models comprise a news classification model, an emotion influence prediction model and an importance degree evaluation model; and analyzing the real-time news data through the plurality of prediction models, and generating a prediction result of the influence of the real-time news data on the transaction on the market. Quantitative analysis of the influence of news events on transactions and markets is realized, so that more accurate and direct data support is provided for transaction decisions.

The application discloses a kind of based on multi-dimensional structure coupling and phase change determination digital content asset evaluation method and system.The method comprises: obtaining multidimensional state data;System state characteristics are constructed;Phase change determination is carried out based on state variable derivative change;When detecting phase change, value transition analysis is carried out;Output value evaluation result.Wherein, the system state characteristics are mapped by multivariate function containing structure coupling degree function, the phase change determination is realized by comparing first derivative or second derivative of state variable with preset threshold, and the value transition analysis is realized by nonlinear model containing piecewise function or exponential function.The application identifies the transition process of digital content from exchange value to structural value, solves the technical problems that the value evaluation dimension is single and the dynamic adaptability is poor in the prior art, and can be widely applied in AI content evaluation, digital asset pricing, public opinion analysis and other fields.

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

The invention discloses a financial market public opinion analysis method, system and device, a medium and a product, and relates to the field of public opinion analysis, and the method comprises the steps: carrying out the preprocessing of multi-source data, and determining the preprocessed multi-source data; the multi-source data comprises financial market negative public opinion data, city investment data, announcement files, industry and commerce data and enterprise internal business data; based on a large model technology, classifying the preprocessed multi-source data by using a public opinion analysis model special for the financial field, and determining a subject type; the main body types comprise city investment enterprises and non-city investment enterprises; according to the subject type, a structured public opinion analysis report is automatically generated, the method has efficient data processing capacity, the analysis efficiency can be improved, and trend prediction and decision suggestions can be provided.

The invention relates to a time sequence prediction model construction method and system fusing macroscopic factors and public opinion analysis. The method comprises the following steps: acquiring public opinion related data and macroeconomy related data; adopting a vector embedding method to generate a public opinion vector and a macroeconomic vector; analyzing the correlation among the vectors to obtain vector correlation features; obtaining a vector conduction mode through conduction modeling; in combination with a classic economic cycle division standard, a vector cycle conduction mode is obtained through cycle collaborative conduction analysis; and embedding the vector periodic conduction mode into a preset time sequence framework to construct a time sequence prediction model. By adopting the method, the accuracy and robustness of time sequence prediction can be improved by deeply fusing the macroscopic factors and the public opinion dynamics.

The present disclosure relates to a GPT-based directional label public opinion analysis method, which comprises: in response to a data label configured for a target user, obtaining first public opinion data of the target user for the data label; based on GPT and the first public opinion data, obtaining a public opinion theme and an emotional category for the first public opinion data; and generating a public opinion analysis result according to the public opinion theme, the emotional category and user information of the target user. Thus, according to the data label set by the user, the event in the target field can be monitored, that is, the first public opinion data is acquired and directional public opinion analysis is performed, which can reduce the processing amount of the first public opinion data and improve the public opinion analysis efficiency. On the other hand, the public opinion analysis result does not contain irrelevant data, thereby improving the accuracy of the public opinion analysis result and avoiding interference of irrelevant data on the user.

The system according to this embodiment aims to understand employee satisfaction in detail and to grasp the state of the organization in real time. [Solution] The system according to the embodiment comprises a question addition unit, an opinion collection unit, an opinion analysis unit, a result aggregation unit, and a solution presentation unit. The question addition unit adds open-ended questions. The opinion collection unit collects opinions from employees. The opinion analysis unit analyzes the opinions collected by the opinion collection unit. The result aggregation unit aggregates the results based on the opinions analyzed by the opinion analysis unit. The solution presentation unit presents solutions based on the results aggregated by the result aggregation unit.

The present invention discloses a method and system for jointly extracting events and event relationships based on dynamic graph propagation. The present invention constructs a multi-level dynamic graph structure and introduces a gate mechanism to realize the dynamic transmission and joint update of information between event internal elements and events, thereby realizing end-to-end collaborative learning of subtasks such as event trigger word recognition, argument recognition, role classification, event type determination and event relationship classification. The present invention simultaneously completes the prediction of event elements, event types and event relationships through dynamic graph propagation, effectively captures contextual dependencies, and has significant advantages in processing nested events and long-distance event relationships. It effectively improves the overall robustness and structural modeling capabilities of the event extraction system and can be widely used in financial public opinion analysis, medical event tracking, judicial case reasoning and other fields.

The embodiment of the invention provides a public opinion early warning method and device, and relates to the technical field of public opinion analysis. The method comprises the steps of obtaining public opinion information of a target user, wherein the public opinion information comprises published target content and historical behavior data of the target user; predicting a first emotional tendency of the public opinion information based on the target content and the historical behavior data by using an emotional analysis model; based on the first emotional tendency, determining a risk existence condition of the public opinion information; and generating early warning information corresponding to the public opinion information in response to the existence of the risk in the public opinion information. Therefore, multi-modal public opinion analysis can be performed based on the multi-modal information of the target user, such as published target content and historical behavior data, so that emotion tendency and potential risk of user public opinions can be accurately identified and early warned.

The invention relates to the field of public opinion analysis, in particular to a financial public opinion sentiment analysis method based on multi-modal dynamic weight fusion, and the method comprises the steps: obtaining multi-modal data input in a financial public opinion scene, and carrying out the feature extraction of different modal data input; based on a self-adaptive attention fusion mechanism, dynamically calculating emotional contribution weights of different modes in a current financial scene, and performing weighted fusion on multi-mode input; classifying the multi-modal features by adopting an emotion classifier to obtain emotion polarity and a continuous emotion intensity value; performing abnormal conflict detection and secondary verification on different modal inputs; and performing dynamic confidence coefficient calibration, and writing the emotional intensity value into a time sequence database in real time. According to the financial public opinion sentiment analysis method, the judgment accuracy and robustness of the financial public opinion sentiment analysis method are comprehensively improved through the design of pre-construction of the financial scene template library, the abnormal conflict detection and secondary verification mechanism, the time attenuation factor and the behavior confidence coefficient calibration mechanism.