54 results about "Topic analysis" patented technology

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 topic analysis method based on a large language model. The method comprises the following steps: performing data preprocessing on dialogue content; performing topic classification on the preprocessed dialogue contents, and collecting contents belonging to the same topic together; discussion objects, abbreviations and key phrases appearing in the content are extracted, and additional background knowledge is collected; summarizing contents under the topic by using a large language model to form a topic name and a topic abstract; and performing the same processing on a plurality of topics in the dialogue process to form a topic analysis report of the discussion content. Important information can be efficiently and accurately extracted, and a topic report is formed.

The present invention belongs to the field of natural language processing technology and intelligent knowledge retrieval technology, and provides an open-ended question implicit intention recognition method and system based on multi-agent reasoning. The technical solution is to use multi-language translation agents and information retrieval agents to construct multi-language background knowledge; based on the constructed multi-language background knowledge, conduct topic analysis, generate multi-domain expert agent information, and dynamically construct multi-domain expert agents; according to the guiding questions raised by the host agent, use multi-domain expert agents to generate their own implicit intentions and reasoning text content according to the guiding questions raised by the host agent; use the host agent to evaluate the quality of the implicit intentions and reasoning text content, and use the speech of each screened expert agent as a node to construct an implicit intention library. Effectively solve the deficiencies in processing complex semantic understanding and other aspects, and more accurately and comprehensively identify the implicit intentions in open-ended questions.

The invention discloses a digital financial management system based on big data, and relates to the technical field of financial management. Comprising a data acquisition module for acquiring financial related data through a web crawler unit and a data interface unit and storing the financial related data in an original database; the data preprocessing module is used for performing cleaning, word segmentation and labeling operation on the original data to obtain text data; and the topic analysis module is used for analyzing the preprocessed text data, identifying hot topics, judging sources of the hot topics and adding feature tags. The system covers a plurality of modules of data acquisition, preprocessing, topic analysis, trend analysis, matching comparison, scoring mechanism, investment suggestion generation and the like, and a complete digital financial management ecology is formed. The system can identify hot topics, judge sources of the hot topics and provide personalized investment suggestions according to risk preferences and market conditions of investors; and the satisfaction degree and the investment success rate of investors can be improved.

The invention provides a social listening data noise reduction method and device based on a large language model, and belongs to the technical field of social media data processing. The method comprises the steps that social media data are collected and preliminarily filtered to obtain preliminarily filtered data; generating a semantic embedding vector by using a pre-trained large language model, and screening semantic related data in combination with cosine similarity and context; identifying and filtering duplicate and variant contents through an embedded model and a generative adversarial network to obtain duplicate-removed data; performing multi-modal feature extraction on the de-duplicated data, and screening credible data in combination with generative AI detection and sentiment analysis; and filtering semantic inconsistency and non-topic continuation texts by adopting a clustering algorithm and semantic relationship analysis to finish noise reduction. According to the method, through a multi-stage and multi-dimensional noise reduction strategy, the purity and credibility of social listening data are effectively improved, and high-quality data support is provided for subsequent topic analysis and decision making.

The present invention discloses a knowledge point extraction method and system based on a large model, which comprises the following steps: obtaining a knowledge text to be extracted, and obtaining important words of the text through large model preprocessing; vectorizing the important words of the text obtained through preprocessing to obtain text word vectors, and obtaining a text vector data set through aggregation; clustering analysis of the text vector data set to divide the text vector data set into different clusters; analyzing the text word vectors, selecting keywords representing the theme of the cluster from each cluster as the knowledge theme of the cluster; analyzing the degree of association between each important word of the text and the knowledge theme in each cluster, and extracting the important words of the text that meet preset conditions as the knowledge points of each cluster; and continuously aggregating and updating the knowledge points extracted from each cluster to obtain a final knowledge point set. The present invention can extract useful knowledge information from text data, and help better understand the content and theme of the text data by continuously updating and improving the knowledge point set.

This invention relates to a big data topic analysis method based on an embedding model. First, the Sentence-BERT model is used to perform sentence embedding representation on preprocessed Chinese text data. Then, the UMAP projection dimensionality reduction algorithm is used to reduce the dimensionality of the embedded vectors. Next, the HDBSCAN clustering algorithm is used to cluster the dimensionality-reduced vectors. Based on the assignment of each Chinese text in the target Chinese dataset to a corresponding topic class, the Chinese words with the highest c-TF-IDF scores are selected to represent each topic class. Finally, the DSG model is used to perform word embedding representation on the topic words, calculating the similarity between different topic words and between different topic classes, thereby detecting the volatility of newly emerging topic classes. The entire scheme design has higher topic consistency and topic diversity, and can detect new hot topics in a timely and accurate manner, providing early warnings.

This application relates to a method, apparatus, computer device, and storage medium for detecting hidden encryption attacks. The method includes: performing topic analysis on input text content to determine the topic semantics of the text content; performing intent parsing on the text content to extract multiple intent information contained in the text content and obtain the intent semantics corresponding to each intent information; performing anomaly analysis based on multiple intent semantics to determine abnormal intent information; determining the semantic similarity between the intent semantics of the abnormal intent information and the topic semantics, and identifying abnormal intent information with a semantic similarity lower than a preset similarity threshold as irrelevant intent information; and performing instruction nature identification on the irrelevant intent information to determine the hidden encryption attack detection result of the text content. Using this application can improve the ability to identify malicious content with high concealment and semantic complexity.

The present invention discloses a keyword-related topic analysis method based on local word embedding technology. When a user enters a keyword, the method uses global word embedding technology to analyze the keyword in text big data, sorts the cosine similarity between the vocabulary and the keyword in the data, and selects the subject words. The subject dictionary is constructed based on the subject words, and the text big database is segmented by time, author, article type, etc. The local word embedding analysis is performed on the keywords and the subject dictionary within the segment, and the local cosine similarity in each segment is used to obtain the theme of each segment. Then, the theme change of the keyword in the text big database can be analyzed. The present invention can be widely used in the fields of social science, public opinion monitoring, historical document analysis, etc., and provides an efficient and accurate theme dynamic analysis tool for text mining.

The application provides a self-adaptive LDA topic model training system based on public opinion real-time data flow, comprising a data gathering module, a data preprocessing module, a self-adaptive LDA model training module and an incremental LDA model fusion module; the data gathering module is used for extracting and converting and loading the structured and semi-structured data and inputting into a distributed message bus kafka; the data preprocessing module is used for preprocessing the data in the message bus kafka and finally forming a weighted word vector; the self-adaptive LDA model training module is used for training to obtain an LDA model result and merging the training result; the incremental LDA model fusion module is used for fusion training to generate a new round of LDA model. The application is superior to the traditional LDA topic analysis method in accuracy and performance, and is applied to network public opinion field event detection, recommendation, word cloud and retrieval and other practical engineering projects, and creates commercial value.

The application relates to a customer complaint text classification method and device, computer equipment and a storage medium. The method comprises the following steps: preprocessing text data to be processed to obtain a plurality of word segmentation results; performing vectorization processing on the plurality of word segmentation results to obtain first vector values of the word segmentation results; inputting the plurality of word segmentation results into a pre-trained topic analysis model to obtain second vector values of each customer complaint topic to which the text data to be processed belongs and third vector values of keywords of the text data to be processed; performing splicing processing on the first vector values, the second vector values and the third vector values to obtain splicing features, and taking the splicing features as features of the text data to be processed; and inputting the features of the text data to be processed into a topic classification model and a perception classification model of a pre-trained classification model respectively to obtain a customer complaint topic category and a customer complaint perception category to which the text data to be processed belongs. The method can deeply analyze specific customer complaint contents under a field category.

The invention provides a social governance-oriented agent non-inductive management method, which comprises the following steps of: acquiring data related to social events from a plurality of information sources, analyzing the acquired data by utilizing a natural language processing technology and a large language model, and extracting key information related to the social events; subject analysis is carried out on the extracted key information, the key information is converted into a specific governance problem, and the governance problem is split from multiple angles; based on the split governance problem, data related to the current event is retrieved from a plurality of data sources, deep analysis is carried out through expert reasoning, an expert reasoning analysis result is generated, the data sources comprise a pre-constructed knowledge graph, social media and an instant webpage, and expert reasoning adopts a multi-round dialogue reasoning mode; and on the basis of the expert reasoning analysis result, updating a figure portrait, automatically generating a social governance report meeting requirements, and displaying related geographical location information through a geographical information system.

The present invention relates to a method and a system utilizing a large language model for topic allocation based on quotations. According to the present invention, a computer-implemented method utilizing a large language model for topic allocation based on quotations may comprise the steps of: specifying content to be analyzed, which includes at least one sentence; specifying at least one topic related to the at least one sentence included in the content to be analyzed; extracting, by using a large language model, at least one quotation corresponding to each of the at least one topic from the content to be analyzed; and providing, by using the topic and the quotation, a topic analysis result for the content to be analyzed.

The present technology assists a user in having an effective conversation in remote multiplex conversation.A display processing unit performs processing of displaying, on the basis of a result of topic analysis processing of identifying an ongoing topic on the basis of multiplex conversation data and giving a title indicating a summary of each topic, the ongoing topic using the title. The user in remote multiplex conversation can easily recognize not only a topic in which the user is participating but also other topics that are ongoing at the same time, and can easily find an ongoing topic of interest and newly participate in the topic. The present technology is therefore capable of assisting the user in having an effective conversation in the remote multiplex conversation.

The invention relates to the field of natural language processing, and discloses a Chinese language and literature text topic analysis system based on big data, and the system comprises a core semantic primitive library construction module which is used for extracting and constructing a knowledge base containing semantics, culture and timestamps from a meta-poeia corpus offline; the text multi-dimensional analysis module is used for extracting four-dimensional features of grammar, core semantics, intertext and emotion time sequence of the to-be-analyzed text in parallel; the feature collaborative analysis module is used for fusing multi-dimensional features, performing context dynamic activation on primitives, and innovatively quantifying and detecting deep semantic conflicts by calculating the dispersion of feature vectors in a semantic space; and the topic report generation module is used for synthesizing a macroscopic topic, integrating all analysis results and generating a structured multi-level report. The system can objectively and deeply reveal complex and even contradictory theme connotation in literature works through modular cooperation, and the accuracy and depth of text analysis are remarkably improved.

The invention discloses an NLP-based method for realizing automatic transcription of hot word extraction specifications, which comprises the following steps of: presetting different scenes, defining hot word types in the scenes and constructing a common hot word library, and preprocessing and cleaning a document; automatically analyzing the cleaned context data, and automatically extracting hot words in the corresponding file content; the extracted hot words are intelligently classified, and a dynamic hot word library is constructed; meanwhile, a voice transfer result is transferred into a hot word verification module in real time; carrying out semantic analysis according to the context of the voice recognition result, judging the most coincident hot word, and carrying out replacement display on the extracted hot word result; according to the method, a normalized result of hot word replacement is displayed, a text result of speech recognition is automatically adjusted, weights of hot words in a dynamic hot word library are updated in real time, representative hot words are extracted from preprocessed texts based on a trained NLP semantic model by applying technologies such as a clustering algorithm and topic analysis, and the accuracy of transcription is improved.

The invention provides a method for automatically generating and distributing network guidance content based on a large model, which comprises the following steps of: 1, data monitoring: collecting related data of a monitoring target in the Internet; 2, analyzing a hot topic, processing the related data collected in the step 1, and obtaining the topic of the hot event and the corresponding data content; step 3, guide content generation: setting a caliber requirement of the guide content according to the theme of the hot event obtained in the step 2, and generating the guide content through a large language model; and 4, task distribution management: selecting a distribution method and a distribution strategy according to the guide content obtained in the step 3, and carrying out guide content distribution.

The application relates to an open domain long text classification method and device based on theme analysis. The method comprises the following steps: constructing a field self-adaptive word table; preprocessing original long text to obtain purified text; using an LDA model to construct a latent theme space; extracting a core sentence group from the long text through semantic clustering to generate an abstract; mapping the abstract to the theme space to calculate a correlation degree score and output a theme identification; and converting the theme identification into a business classification label by querying a theme-label mapping table. The application effectively solves the problems of complex long text semantics and dynamic label system changes, and improves classification accuracy and expansibility.

The invention discloses a keyword related topic analysis method based on a local word embedding technology, which comprises the following steps of: after a user inputs a keyword, analyzing the keyword in text big data by utilizing a global word embedding technology, and sorting and screening out a topic word according to cosine similarity of vocabularies and the keyword in the data; the method comprises the following steps: constructing a subject dictionary through subject terms, segmenting a text big database according to modes of time, authors, article types and the like, performing local word embedding analysis on keywords and the subject dictionary in segments, and obtaining a subject of each segment through local cosine similarity in each segment; and furthermore, the theme change condition of the keyword in the text big database can be analyzed. The method can be widely applied to the fields of social science, public opinion monitoring, historical literature analysis and the like, and an efficient and accurate theme dynamic analysis tool is provided for text mining.

The invention discloses a network public opinion monitoring method and device and a storage medium, and relates to the technical field of the Internet, and the method comprises the steps: obtaining post information issued by each user; performing data cleaning on the post information according to a sensitive word database updated in real time; the post information after data cleaning is analyzed according to multi-dimensional monitoring indexes, public opinion monitoring is conducted on the post information according to the analysis result, and the multi-dimensional monitoring indexes comprise the flow analysis index, the topic analysis index and the sentiment analysis index of the post. According to the method, data cleaning is performed on the post information by using the sensitive word bank updated in real time, so that the capturing speed of public opinions is increased. And the post information is analyzed according to the flow analysis index, the topic analysis index and the sentiment analysis index, and the accuracy of analyzing the real sentiment and the focus of the user is improved based on a multi-dimensional analysis result, so that the accuracy of network public opinion monitoring is improved.

The invention relates to the technical field of medical big data processing, in particular to a neurological critical patient follow-up visit management method and system. The method comprises the following steps: acquiring complaint data of a patient; quantitatively evaluating the health status of the patient by using a questionnaire to obtain a first score; if the first score is greater than a questionnaire threshold value, performing topic analysis on the chief complaint data to obtain a first topic; performing topic analysis on a question corresponding to the chief complaint data to obtain a second topic; calculating the semantic similarity between the first theme and the second theme; constructing a definition vector based on the semantic similarity and the first score; calculating a first similarity between the definition vector and a preset standard vector; when the first similarity meets the first range, risk assessment is carried out based on the medical information, the medical report and the chief complaint data of the patient, intervention measures of the patient are determined according to an assessment result, the recovery state of the neurological function of the patient can be continuously tracked, and medical staff are helped to comprehensively know the rehabilitation process of the patient.

The present application provides an interest identification method, apparatus, device and storage medium, which relates to the field of big data. The method includes: obtaining voice call data, converting the voice call data into text data; constructing a heterogeneous graph based on the text data, and determining the high-frequency words in the heterogeneous graph by statistical methods; classifying the heterogeneous graph by means of a graph neural network to obtain at least one category corresponding to the text data; performing topic analysis on the documents in each category by means of latent Dirichlet distribution to obtain the topic of each category and the keywords in each topic; determining user interests based on the keywords and high-frequency words in each topic. The method of the present application realizes the rapid and accurate identification of user interests, avoids the inaccuracy of manual identification of user interests and the corresponding waste of personnel, reduces the waste of manpower, reduces the time of user interest analysis, and improves the efficiency of user interest identification.

This invention discloses a method and apparatus for predicting the emergent behavior of groups based on social network evolution computation. By deeply analyzing the evolutionary laws and group behavior characteristics of social networks, and comprehensively considering the evolutionary laws, group behavior characteristics, and interactions between individuals, this invention can simultaneously simulate the evolutionary process of social networks and changes in group behavior from two dimensions: network structure and node properties. This overcomes the limitations of traditional methods that only analyze from a single dimension, thus significantly improving the accuracy and reliability of group behavior emergent prediction. Furthermore, this invention can accurately identify key nodes, information propagation paths, and driving factors of group behavior. By combining pre-trained models and sentiment dictionaries to analyze text data, it improves the accuracy of sentiment recognition and topic analysis, thereby enhancing the targeting and precision of group behavior prediction.

This application discloses an article classification method, apparatus, electronic device, and storage medium, relating to the field of artificial intelligence technology or other related fields. The method includes: acquiring at least one target article to be classified; performing topic analysis on the target article using a preset model to generate topic tags; calling a classification tag document and querying the domain to which the topic tags belong in the classification tag document to obtain the domain tags of the target article; querying all topics involved by the topic tags and domain tags in the classification tag document to obtain a candidate topic set, and combining the preset model and the candidate topic set to perform topic analysis on the target article to generate topic tags; summarizing the domain tags, topic tags, and topic tags to generate the article classification result for the target article. This application solves the technical problem in related technologies where reliance on manual labeling and fixed classification rules leads to low accuracy in article classification.

The invention discloses an integrated energy service-oriented data resource theme extraction method and system, and belongs to the field of integrated energy services. Based on a BERTopic topic analysis method, by inputting a certain scale and quantity of comprehensive energy service practice representative cases, topics and keywords thereof are extracted from a large amount of text data, the centralized distribution condition of each topic data set is analyzed, the importance of the topics is judged, a user is helped to intuitively understand a topic structure in the data, and the user experience is improved. And main physical boundaries, device contents and key technical elements of the system are extracted.

The embodiment of the invention provides a comment information publishing method and device and a storage medium. A workflow engine integrated in an AI agent is used in combination with an MCP tool specially developed for calling a large-scale language model, and a workflow for automatic comment is defined in the workflow engine; the AI agent calls the MCP tool by depending on a large language model to complete the execution of the workflow; specifically, in response to a comment trigger event, target scene information of a post to be commented is determined; and generating model cue words based on the dependency relationship of the workflow and the MCP tool, inputting the model cue words into the large language model, and guiding the model cue words to execute the steps of post retrieval, post acquisition, topic analysis, comment generation and comment release included in the workflow. And the corresponding MCP tool is called by the large language model in the step of having the dependency relationship with the MCP tool, and the comment information is automatically generated and published to the service platform, so that the efficiency and the accuracy of publishing the comment information are improved, and the customer satisfaction is further improved.

The invention relates to a deep research method and device of a script, a storage medium and electronic equipment. The method comprises the steps of performing structured analysis on a to-be-deeply researched script text to extract script analysis data including role information, session information and candidate themes, and generating a script theme set according to the script analysis data; generating at least one corresponding target audience portrait for each script theme in the script theme set; retrieving and analyzing social public opinion data associated with each target audience portrait to obtain public opinion analysis result data, and performing matching analysis on the public opinion analysis result data and the script theme set to obtain topic analysis result data; and performing aggregation and consistency verification processing on the public opinion analysis result data and the topic analysis result data to obtain a structured deep research report of the script text. According to the method and the device, the technical problem of lack of iterable, multi-dimensional linkage and verifiable script deep analysis is solved.

The present invention relates to the field of data mining technology, and discloses a data mining system based on an AI large model, including a data preprocessing module, a feature extraction module, a rule definition module, and a mining theme analysis module; the data preprocessing module preprocesses the original file data and the data in the original database, and the preprocessing includes removing duplicate data and processing missing data, and the processed data constitute a basic database. The present invention determines the degree of adaptation of the screened target theme and the mined data in the basic database through the consistency of data types, the degree of string matching, and the similarity of fuzzy matching between the data in the basic database and the target theme data provided by the mining theme analysis module, and the similarity of fuzzy matching between the two data, and preferentially outputs the mining data with the highest degree of adaptation as the output target. This design can greatly improve the accuracy of mining and outputting data.

The invention discloses a data collaborative analysis method based on a unified index view, and relates to the technical field of data governance and intelligent analysis. Long-term pain points of disordered index definition, inconsistent analysis calibers and inefficient cross-department cooperation in an enterprise multi-source heterogeneous environment are effectively solved, the credibility of data assets is improved, a unified index model solidifies calculation rules and statistical calibers from the source, core indexes such as sales and user retention rates are enabled to be semantically consistent in the whole platform, and the system performance is improved. Secondly, the collaborative analysis efficiency is released, business personnel do not need to repeatedly confirm the meaning of the index, a cross-department comparable report can be quickly constructed based on a preset theme analysis view, meanwhile, a technical team greatly reduces the operation and maintenance cost through log binding and change tracing, in addition, the technical adaptability can be enhanced, and the method is suitable for a multi-source heterogeneous data environment. The platform flexibly responds to business scene changes through a dynamic mapping strategy, and the problem of stiffness of the index tree is avoided.

This invention discloses a visualization analysis method, apparatus, device, and storage medium for multi-indicator comparison. The method includes: in response to a user's topic configuration operation in the visualization analysis interface, creating a target analysis topic and binding a target analysis chart to the target analysis topic; in response to a user's indicator selection operation in the visualization analysis interface, selecting at least one target indicator from a unified indicator library, aligning each target indicator with the standard dimensions of the target analysis chart, and then displaying them superimposed in the same coordinate system; wherein the indicators in the unified indicator library are all constructed based on predefined indicator standard models; and based on the target indicators superimposed in the same coordinate system, performing analysis using a preset multi-level indicator comparison strategy to obtain multi-level analysis results corresponding to the target analysis topic. Using this invention can effectively shorten the topic analysis cycle and improve topic analysis efficiency.