22 results about "Topic analysis" patented technology

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 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 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 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 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.

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 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.

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.

The invention discloses a voice interaction method, device and equipment, and the method comprises the steps: recognizing the voice of a current interaction request, and obtaining the voice of each user; converting the voice of each user into a text; performing semantic analysis on the text corresponding to the voice of each user, extracting keywords, and performing topic analysis on the text corresponding to the voice of each user to obtain topics corresponding to the text; and calling a large language model, generating a personalized reply based on the text, the keyword, the information stored in the two-dimensional storage structure and the information stored in the global structure of the user in the current round of interaction, and converting the generated personalized reply into voice to be output. According to the method, parallel management and response to multiple topics are realized, the problems of semantic loss and context misjudgment are effectively solved, and the practicability and user experience of intelligent equipment in a complex social scene are enhanced.

The invention discloses an unreasonable medical examination judgment method, system and device and a storage medium, and the method comprises the steps: firstly building an observation list of examination items through a statistical analysis and cross verification method, and then carrying out the analysis and processing of patient samples related to the examination items in the observation list; performing topic analysis on a patient sample through an LDA algorithm to obtain a disease topic, calculating the correlation between each disease and the mode disease, and performing cross validation on the disease which is remarkably different from the mode disease through the disease topic to obtain a disease result; and judging whether the examination sheet is unreasonable or not under the condition of the illness, so that the system can perform unreasonable examination, monitoring and early warning on the condition. According to the method, the importance of examination items is considered, judgment of unreasonable examination is carried out on the basis of objective illness condition facts and probability distribution in the examination sheet making process, reasonability and basis are achieved, and the problem that at present, unreasonable examination is difficult to judge is effectively solved.

The application relates to a topic analysis method and device, computer equipment and a storage medium. The method comprises the following steps: extracting a plurality of keywords from conversation data; screening topic keywords from the plurality of keywords; determining associated keywords related to the topic keywords from the plurality of keywords; combining the topic keywords and the corresponding associated keywords respectively to obtain at least one topic phrase; and determining a target topic corresponding to the conversation data based on the at least one topic phrase. The method can improve the efficiency of topic analysis.

The invention relates to an open domain long text classification method and device based on topic analysis. Comprising the following steps: constructing a field adaptive word list; preprocessing the original long text to obtain a purified text; constructing a potential theme space by using an LDA model; extracting a core sentence group from the long text through semantic clustering to generate an abstract; mapping the abstract to a theme space, calculating a correlation score and outputting a theme identifier; and converting the theme identifier into a service classification tag by querying the theme-tag mapping table. According to the method, the problems of long text semantic complexity and label system dynamic change are effectively solved, and the classification accuracy and expansibility are improved.

Disclosed herein are systems and methods for generating a prompt with context based on a list of topics generated for responses from large language models (LLMs). In one aspect, the method includes: obtaining a query from a user; generating and transmitting a prompt based on the query for input into a first and second LLMs; obtaining a first selected portion of the first response from the first LLM and at least a second selected portion of the second response from the second LLM; generating a list of topics using a trained topic analysis machine learning model (MLM) to identify topics from the responses; and generating a prompt for input into the third LLM utilizing at least one topic from the list of topics using at least the selected portions from the first LLM or the selected portions from the second LLM.

The invention belongs to the technical field of travel route recommendation, and particularly discloses a travel route recommendation method based on LLM user portraits and multi-dimensional feature optimization, and the method comprises the steps: calling LLM to analyze explicit and implicit preferences to construct user portraits based on travel demand texts and historical search records; performing topic analysis based on the unstructured text data to determine a topic probability, and generating a static feature vector in combination with the structured information; calculating the matching degree of the portrait and the static vector, and screening candidate points; on the basis of the candidate point coordinates, the travel time consumption and the real-time traffic, path search travel serialization is carried out with the purposes of minimizing the passing time length and maximizing the experience satisfaction degree, and a travel path plan is generated; structured information, BERT emotion and an LDA theme are fused to construct a static feature vector, an entropy weight method is combined with a subjective weight to determine a comprehensive weight, multi-objective optimization is performed through a path planning algorithm accessing real-time traffic information, and personalized recommendation of tourism paths is realized.

The disclosed embodiments provide techniques for generating scripts for TV / Web series using viewer feedback. The proposed system captures free form user input data from social media websites and streaming media platforms, including viewer comments and ratings. System uses advance techniques such as natural language processing (NLP) for sentiment and topic analysis and generative AI for content generation. A feedback loop with professional scriptwriters ensures the generated scripts are refined, polished and finalized with human oversight. Traditional methods of gathering viewer insights, such as ratings and surveys, often lack the ability to capture free form audience opinions and utilize the same to influence the future content development. This AI-driven approach significantly reduces the time and effort required by traditional scriptwriting methods while aligning entertainment content with audience preferences, fostering deeper audience involvement and satisfaction.

The application discloses a public opinion response effect measurement method based on theme migration and emotion change recognition. The method takes relevant microblog hot search blog posts and comment information of an event as basic data, performs theme analysis on the microblog text based on an LDA theme model, calculates the emotion value of the comment content according to a Bi-LSTM model, constructs a measurement model of the response effect under a sudden negative public opinion, and evaluates the response effect based on the recognition results of theme migration and emotion change. The application has the advantages of easy practice, scientific index, strong pertinence and the like, can be used for measuring the response effect in a sudden negative public opinion, evaluating the intervention effect on the evolution of the negative public opinion, analyzing the advantages and disadvantages of the response measures, and providing optimization strategies for optimizing the negative public opinion management work.

The invention relates to the technical field of intelligent retrieval and knowledge service, and discloses an intelligent retrieval and reference material generation method and system for report compiling. The method comprises the following steps: receiving at least one original query keyword input in a report compiling scene, and generating a keyword set based on a subject analysis demand; vectorizing and aggregating the data into a comprehensive semantic vector representing the intention of the report; retrieving by taking the keyword set and the comprehensive semantic vector as a mixed query condition to obtain an initial material; on the basis of the requirement of the report on the credibility of the material, calculating a correlation score by using a rearrangement model, and obtaining a standardized material confidence score through normalization and nonlinear smooth mapping processing; and according to a scoring result, outputting a sorting list which can be used as the alternative reference materials of the report. According to the method, the problems of low efficiency of manual searching and reference material screening and difficulty in credibility verification in report compiling are solved, and the support efficiency and quality of an intelligent retrieval system based on big data in professional content generation are improved.

The present disclosure relates to a natural language processing-based intelligent traffic text analysis method, belonging to the technical field of intelligent traffic, comprising the following steps: text preprocessing, self-defined named entity recognition, topic analysis and clustering, and word embedding and citation analysis; wherein, the target text is segmented, stop words are removed, and text sentence labels are marked; a named entity recognition model is trained, and the named entity recognition model is used to specify information in advance for the summary and title of the target text to filter out the required articles; the topics of the target text are analyzed and clustered, new data sets are created according to the topics, and model evaluation is carried out by using coherence; wherein, word embedding is used to find at least one word as a context from each topic cluster, and the at least one word is used as a keyword to be checked, and when the keyword is set, the keyword is used to find words in each topic cluster that have the same context as the keyword.

The application provides a news matching method fusing theme and entity knowledge, and belongs to the technical field of natural language processing. The method obtains theme and entity knowledge by respectively passing a text to be matched through a theme analysis model and an entity recognition tool, further understands the news text by extracting the features of the theme and the entity knowledge, forms a pseudo twin network, calculates the similarity score of the two, and judges whether they are matched. The method provided by the application can effectively improve the matching accuracy based on various forms of news texts, and is suitable for news relevance matching of news and cases.

Systems and methods for providing enhanced teleconferencing. An example method includes receiving audio streams from a plurality of client devices of participants of a teleconference; converting the audio streams for a first conversation within the teleconference into first text; converting the audio streams for a second conversation within the teleconference into a second text; analyzing the first text to identify one or more topics being discussed in the first conversation; analyzing the second text to identify one or more topics being discussed in the second conversation; and presenting, in a teleconference user interface, at least one of the one or more topics being discussed in the first conversation or the one or more topics being discussed in the second conversation.