498 results about "Sentiment analysis" patented technology

The invention discloses a digital human interaction method and device based on multi-modal sentiment analysis and a medium, and relates to the field of artificial intelligence, and the method comprises the steps: collecting multi-modal data of a user in real time through a multi-source sensor device; the multi-modal data comprises face video stream data, voice audio stream data and text dialogue data; calling data analysis engines corresponding to different modalities, and extracting corresponding modal feature sequences; according to the current interaction scene, the modal feature sequence and the historical dialogue context features are fused, and a comprehensive emotion evaluation result is generated; outputting a corresponding multi-modal response data packet based on the modal feature sequence through an interactive response engine corresponding to a comprehensive emotion evaluation result; and executing the multi-modal response data packet. And after feature fusion is carried out in combination with the current interaction scene, the generated response can more accurately fit the current emotion demand and communication context of the user, so that the digital human can be more easily fused into various scenes needing emotion interaction.

The invention provides a multi-modal sentiment analysis method based on a diffusion model and self-paced learning. The method comprises the following steps: firstly, dividing a data set into a missing image modal data set and a complete modal data set according to image modal integrity; thirdly, constructing a feature alignment diffusion model, and performing image generation; training the diffusion model by adopting a self-paced learning strategy and a missing image data set; and based on the trained diffusion model, guiding a reverse process through text features to generate feature representation of the missing image. And carrying out weighted fusion on the generated image features and text features by using an attention mechanism, and dynamically adjusting contribution weights of all modalities to generate a complete multi-modal feature representation. And finally, integrating a missing modal completion result and the complete modal features to form a unified multi-modal representation, inputting the unified multi-modal representation into a multi-modal sentiment classification module, and outputting a sentiment classification result. According to the method, the problem of multi-modal sentiment analysis under random missing of image modals is effectively solved, and the generation quality and semantic consistency are improved.

The invention relates to the field of multi-modal sentiment analysis, in particular to a multi-modal sentiment analysis method based on task association perception learning. According to the scheme, the method comprises the steps that multi-modal data including text, audio and visual modal data are obtained, feature extraction is conducted on the multi-modal data, a double-branch comparison module is constructed for each kind of modal data, each double-branch comparison module comprises a fusion branch and a comparison branch, and the fusion branch is connected with the comparison branch; input of a fusion branch and a comparison branch of each double-branch comparison module is an extracted text modal feature, an extracted audio modal feature and an extracted visual modal feature; and finally, feature fusion and prediction output are carried out, so that the accuracy and robustness of multi-modal sentiment analysis are improved. The method is suitable for multi-modal sentiment analysis.

The invention requests to protect an emotion analysis method based on a text-guided comparative learning and gating fusion mechanism, which is used for solving the problems of insufficient modal feature representation discrimination and insufficient modal information fusion in multi-modal emotion analysis. Firstly, features of three modes are extracted in a feature extraction module, then a text mode is selected as a dominant mode, a text-guided weighted comparison learning strategy is designed, the strategy screens positive and negative sample pairs through the text mode, and weights are dynamically distributed for hard pairs and easy pairs, so that the model keeps universal features while focusing on the hard pairs, and the model is optimized. Therefore, the discrimination capability of modal feature representation is improved. Secondly, in a feature fusion module, a text-guided gating fusion mechanism is provided, weights of text modals and cross-modal fusion features are adaptively adjusted through a gating unit, and a self-attention mechanism is used to suppress fusion noise, so that the modals are fused more fully. Experiments on public data sets CMU-MOSI and CMU-MOSEI show that compared with a reference method, the method provided by the invention has advanced performance on multiple indexes such as F1, ACC-2 and the like.

The invention discloses a multi-modal sentiment analysis method and device based on feature decoupling and guide correction, and relates to the technical field of sentiment analysis, and the method comprises the steps: building an initial multi-modal sentiment analysis network, the initial multi-modal sentiment analysis network comprises a multi-modal feature extraction mapping block, a cross-modal interaction enhancement coding block, a feature decoupling block, a guide type gating semantic correction block and a low-rank fusion sentiment analysis block, and the total loss value of a training sentiment analysis result for training the multi-modal sentiment data is determined; and performing optimization iteration on the initial multi-mode sentiment analysis network according to the total loss value until a target multi-mode sentiment analysis network is determined, and outputting a target sentiment analysis result of the to-be-analyzed multi-mode sentiment data by adopting the target multi-mode sentiment analysis network. Based on the above scheme, the multi-modal fusion emotion recognition precision is improved.

The invention discloses a multi-mode emotion recognition method and system based on prompt enhancement, a medium and equipment, and belongs to the technical field of emotion analysis, and the multi-mode emotion recognition method based on prompt enhancement comprises the steps: obtaining input data of text, visual and audio modes; the modal-based prompt encoder performs prompt template-based feature enhancement on each modal input data to generate prompt enhanced feature representation; establishing an inter-modal information exchange channel by adopting a cross-modal adaptive alignment mechanism based on prompt enhanced feature representation, and generating alignment dominant features; based on the aligned dominant features, dynamically fusing the high-quality features by adopting a multi-level quality evaluation mechanism to generate enhanced dominant features; and performing multi-modal fusion on the enhanced dominant feature and the other two modal features, and outputting an emotion prediction result. According to the method, different data missing modes are effectively dealt with, the limitation of a rigid interaction mode of a traditional method is solved, the quality and reliability differences of different modes are considered, and robust multi-mode sentiment analysis is achieved.

The invention discloses an emotion analysis method and system based on big language model reasoning chain generation, and the method comprises the steps: obtaining text data, classifying the features of the text data, and determining a graphic reasoning template; reasoning chain generation is carried out step by step through the reasoning template, and risk prediction is carried out on the generated reasoning chain so as to determine a generation strategy of candidate words in the reasoning chain, so that a preliminary reasoning chain result is obtained; and on this basis, calculating the aggregation support degree, finally adjusting the generation strategy again to obtain the inference result of the next step, and obtaining the final inference chain according to the graphical inference template through the cyclic adjustment. Therefore, according to the finally obtained reasoning chain, the illusion situation in the model operation process is greatly reduced, the credibility of the reasoning result is improved, and the stability and accuracy of the generated result are improved through multi-time interactive calculation of the reasoning chain and text data.

A system and method for real-time facial and sentiment detection using a computing system. The system includes a video input module that receives real-time video input from various sources such as webcams, security cameras, and smartphone cameras. The video frames are pre-processed by adjusting the resolution, converting color spaces, and isolating the foreground from the background. A facial detection module employs a convolutional neural network to identify and localize human facial regions within the video frames. Geometric and appearance features are extracted from the localized facial regions by a feature extraction module. A sentiment classification module classifies the extracted features to determine sentiments using a deep learning model. The system also includes a module for API integration, enabling third-party applications to utilize the sentiment recognition results.

The invention discloses an aspect-level sentiment analysis optimization method based on multivariate external knowledge fusion, which relates to the technical field of sentiment analysis optimization, and comprises the following steps: constructing a multivariate external knowledge source comprising a Chinese sentiment dictionary, a domain knowledge graph and a user comment prior mode library; the sentiment module is used for providing vocabulary-level sentiment polarity, entity attribute relations and high-frequency evaluation semantic modes; semantic coding is performed on the input text and the specified aspect words to generate context semantic representation, and global semantic features and local position features are extracted in combination with aspect word position information; based on a semantic coding result, converting the multivariate external knowledge sources into structured knowledge representations, and dynamically adjusting contribution weights of various types of knowledge through a gating fusion mechanism to generate fused knowledge representations; performing dependency syntactic analysis on the input text, constructing an original syntactic structure, and calculating the correlation strength of each grammatical component and aspect words in combination with context semantic representation; and pruning the original syntactic structure according to the correlation intensity.

A system and method for generating structured software application specifications through multi-phase conversational dialogue is disclosed. The system implements multiple specialized AI agents including a framework generation agent, an interactive coaching agent, specialized capsule agents for analyzing different concern categories, and a specification coaching agent. During Phase 1, the system conducts exploratory dialogue using a tailored question framework while detecting and storing user concerns in structured capsule entries. Concern injection into the dialogue is strategically timed based on algorithmic evaluation of cooldown periods and user sentiment analysis. During Phase 2, the system conducts comprehensive concern resolution dialogue and generates a final specification in structured JSON format with explicit traceability linking requirements to source conversations and concern resolutions. The system solves technical problems of preserving concern context across conversation phases, optimizing injection timing to avoid overwhelming users, and generating machine-readable specifications suitable for automated downstream processing.

Provided is a sentiment analysis system including at least one processor configured to: acquire a first comment which relates to a service, and which is input by a user who uses the service; execute clustering relating to the first comment; acquire a sentiment word relating to a sentiment about the service based on an execution result of the clustering; and analyze the sentiment in a first sentence included in the first comment based on the sentiment word.

The invention discloses a multi-modal sentiment analysis method and system based on credibility driving, and belongs to the technical field of artificial intelligence. The system comprises a modal feature decoupling reconstruction module for realizing semantic consistency by constructing a shared routing hybrid expert model to separate modal shared representation and modal private representation and reconstructing modal features; the dynamic modal perception calibration module evaluates the actual contribution degree of each modal through a modal contribution analysis component, quantifies the consistency between the model self-learning weight and the modal contribution value through a consistency supervision constraint component on the basis of a Pearson's correlation coefficient sorting supervision mechanism, and enhances the model reliability; and the gating multi-mode fusion module actively activates potential information of a low-contribution mode through a gating fusion framework, so that the fusion integrity is improved. According to the method, the problems of semantic ambiguity and unreliable fusion weight are relieved by strengthening weak modal contribution, and the reliability and accuracy of the model can be effectively improved.

The invention discloses an Internet of Things industry intelligent customer service supervision and control system based on artificial intelligence, and belongs to the technical field of customer service supervision. Comprising an omni-channel intelligent access and intention understanding module, an AI intelligent center and complex decision module, an intelligent supervision and ethical regulation and control module, a man-machine cooperation and humanistic care module, a data-driven optimization and edge intelligent module and a security privacy and controllable treatment module, and the omni-channel intelligent access and intention understanding module is used for integrating multiple channels. Unified request distribution is achieved, data input by a user are analyzed through voice recognition, NLU natural language understanding and image recognition technologies, composite intentions are accurately captured, interaction strategies are dynamically adjusted in combination with device state data, user historical behaviors and real-time positions, and pacified talking skills or manual intervention are triggered through voiceprint / text emotion analysis. On the basis of realizing customer service supervision and regulation, all-channel fusion and intention accurate analysis can be realized, and ethical safety integrated design can be realized.

The invention discloses a multi-modal sentiment analysis model based on hierarchical adaptive cross-modal fusion, belongs to the field of natural language processing, and is used for solving the problems that in the prior art, multi-granularity sentiment feature extraction is insufficient, a cross-modal fusion mechanism is rigid, and the distribution difference between different-source modals is large. The method comprises the following steps: firstly, extracting features of texts, audios and visual modalities from original video data, and coding the features into advanced semantic features; secondly, multi-granularity information is fused through a hierarchical feature extractor to generate enhanced single-mode features; then, a self-adaptive cross-modal fusion network with a text as a core is adopted to realize bidirectional interaction and dynamic weighted fusion between modals; further, a dynamic contrast learning mechanism is introduced to align modal distribution in a unified potential space; and finally, inputting the optimized multi-modal features into a classifier and outputting an emotion analysis result. According to the model, through collaborative optimization of multi-granularity feature extraction, adaptive fusion and comparative learning, the accuracy of sentiment analysis and the robustness of the model are remarkably improved.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal sentiment analysis method and system based on feature decoupling and variational optimization, and the system comprises a feature decoupling module, a modal variational alignment module, an adversarial representation module and a multi-path interactive fusion network. According to the system, modal features are separated into modal specific features and modal invariant features through a feature decoupling technology, uncertainty modeling and re-parameterization processing are carried out on exclusive features by adopting a single-modal uncertainty perception fusion technology, and robustness of feature learning is enhanced by adopting an adversarial training mechanism for shared features. Model optimization is carried out by combining a cross-modal attention mechanism modeling inter-modal interaction relationship and a multi-level loss function, so that efficient feature extraction, robust feature fusion and accurate emotional intensity prediction of multi-modal data are realized.

The invention provides a multi-modal sentiment analysis model and method, electronic equipment and a medium, and the model comprises a feature enhancement module which is used for extracting original multi-modal data features through an exclusive tool, constructing a graph structure, and enhancing the graph structure through a graph convolutional network to obtain multi-modal enhanced features; the modal multi-stage balance module is used for processing enhanced features by using different multi-stage network structures and outputting multi-modal consistency representation; the modal noise reduction decoupling and specificity recombination module is used for obtaining low-noise representation through a modal noise reduction decomposer based on the global information and recombining the low-noise representation in a modal bank to generate low-noise multi-modal specificity representation; and the hierarchical fusion prediction module is used for fusing consistency and specificity representation according to single-peak, double-peak and three-peak modes, and outputting an emotion prediction result through a multi-layer perceptron.

The invention discloses a multi-modal sentiment analysis method combining dynamic sentiment knowledge and a sparse attention mechanism. The method comprises the following steps: acquiring and preprocessing text, image and audio multi-modal training samples; extracting preliminary features of each modal by using the pre-training model; emotion feature expression is enhanced through an emotion related feature extraction module; the enhanced features are input into a Transform Encoder, and an emotion knowledge vector is dynamically generated; guiding multi-modal feature semantic alignment by using the vector; reserving the first k maximum values of the attention score by adopting a sparse attention mechanism, and realizing effective fusion of modal features; and finally, outputting an emotion analysis result through the emotion classifier. According to the method, a multi-task joint loss function is designed, and end-to-end optimization is carried out in combination with classification loss, alignment loss and comparison loss. According to the method, the problems of difficulty in modal alignment, much fusion redundant information and insufficient cross-domain adaptability in multi-modal sentiment analysis are effectively solved, and the accuracy and generalization ability of sentiment analysis are remarkably improved.

The invention provides a fine-grained sentiment analysis-oriented sentiment data automatic labeling method and system, and belongs to the field of artificial intelligence and sentiment analysis. According to the method, coarse-grained emotion pre-classification and confidence weighted fusion are carried out by extracting text, voice and visual features; fine-grained emotion recognition is realized by combining large language model reasoning and spectral clustering; optimizing a result by utilizing a conflict resolution mechanism, and generating a final label through multi-level voting integration; and finally, through multiple dimensions of multi-modal consistency, feature space outlier degree and conflict resolution decision effect, evaluating the credibility of the final label, and identifying a low-credibility sample. According to the method, progressive analysis from coarse granularity to fine granularity is realized, the problems of modal isomerism, information conflict and low labeling credibility are effectively solved, the manual labeling cost is reduced, and the accuracy and reliability of sentiment analysis are improved.

The invention discloses an emotion analysis method based on a multi-modal large model. The method comprises the following steps: S1, extracting multi-modal emotion features; respectively designing special emotional feature extractors for three modes of facial expression, voice and text; s2, carrying out cross-modal emotion alignment and fusion; mapping the emotion features of different modes to a unified emotion semantic space; s3, an emotion inconsistency detection mechanism; the method is specially used for detecting the emotion inconsistency phenomenon between different modes. S4, fine-grained sentiment classification is carried out; the sentiment classifier comprises three sub-tasks of basic sentiment classification, complex sentiment recognition and sentiment intensity regression; s5, a model fine tuning training strategy; and optimizing the performance of the model by adopting a multi-stage fine-tuning training strategy. According to the method, deep fusion and accurate analysis of facial expressions, voice acoustic features and text semantic information are realized, so that a complex emotional state and an emotional inconsistency phenomenon are effectively recognized.

The invention relates to the technical field of face recognition, and discloses a multi-modal alignment training-based face sentiment analysis method and system, and the method comprises the steps: obtaining multi-source data, carrying out the preprocessing of the multi-source data, obtaining a training set, constructing a basic model for the face sentiment analysis, selecting a sample with an inference text to generate a small number of high-quality sentiment analysis samples, and carrying out the recognition of the high-quality sentiment analysis samples; supervising and finely adjusting the model; selecting a sample with a facial action unit label and an emotion label, and performing reinforcement learning training on the model in combination with the predicted accuracy of the facial action unit label, the emotion label and the reasoning text; and training the basic model by using the training set, expanding the original training set by using the output of the trained basic model to obtain a new training set, continuously training the model, stopping training until a preset condition is met to obtain a final basic model, and performing facial sentiment analysis by using the final basic model. According to the method, illusion can be controlled, the accuracy of a facial emotion analysis result is improved, and the data set construction cost is reduced.

An information processing method includes: obtaining, as text information, information related to communication by a person; performing emotion analysis on a plurality of words or phrases after breaking down the text information into the plurality of words or phrases by performing morphological analysis on the text information; and visualizing an analysis result of the emotion analysis according to each row and column of a matrix table by forming the matrix table by arranging, in a first direction, a plurality of emotional expression-related items that represent human emotions and arranging, in a second direction, an attribute category-related item that indicates an attribute of the person or an organization.

A system is provided.SOLUTION: A system comprising: means for collecting voice data of a workplace in real time and transcribing the voice data; means for analyzing the transcribed data and performing emotion analysis; means for determining a degree of unpleasantness based on a result of the emotion analysis; and means for storing related data and notifying a compliance department when the degree of unpleasantness exceeds a specific threshold.SELECTED DRAWING: Figure 1

The invention discloses a digital art exhibition space display interaction method and system based on a VR technology, and relates to the technical field of exhibition man-machine interaction, and the method comprises the steps: carrying out the time sequence alignment processing of a multi-source asynchronous data flow, and constructing an emotional state confidence curve, an interest concentration curve and a cognitive load curve; constructing a user emotion space vector based on the tangential direction of the curve and the instantaneous value; and calculating a difference value between the user emotion state and the exhibit semantic emotion confidence coefficient, and respectively adopting different strategies according to the difference value: when the difference value is large, directly generating display content, and when the difference value is small, judging whether to execute deepening interaction or correct an emotion space vector through an interest maintenance coefficient. Through multi-modal data fusion and dynamic sentiment analysis, the problem that the existing VR exhibition content is static and fixed and cannot adapt to the user state in real time is solved, intelligent content display based on user sentiment change is realized, and the immersion and individuation level of exhibition viewing experience are remarkably improved.

The invention provides an intelligent evaluation system for an ideological and political education effect based on adaptive feedback, and belongs to the technical field of real-time evaluation of the ideological and political education effect. Comprising a multi-modal data acquisition module, a dynamic emotion modeling module, an ideological and political target adaptation evaluation module, a multi-dimensional feedback intervention module, a teaching resource intelligent recommendation module and a teaching effect tracing module. According to the invention, a multi-modal sensor is integrated, non-blind area data capture of a teaching scene is realized, and the comprehensiveness of emotion data acquisition is improved; on the basis of a double-stage hypergraph network and a dynamic weight adjustment mechanism, the generalization ability of the model is enhanced in combination with adversarial training, and cross-scene refined sentiment analysis is achieved; environmental interference is stripped through a net effect equation, a dynamic threshold value is matched with an ideological and political knowledge graph, the long-term teaching effect is scientifically quantified, and self-adaptive evaluation is provided.

The invention discloses a multi-modal sentiment analysis system and method based on dynamic routing and feature decoupling, and relates to the field of artificial intelligence and sentiment calculation. The system comprises a feature decoupling module, a decoupling guide routing module and a dynamic attention fusion module. The method comprises the following steps: firstly, decomposing an original feature of each input mode into a mode sharing feature and a mode specific feature through a feature decoupling module; aggregating the modal sharing features to obtain enhanced modal sharing features; a decoupling-guided routing module generates a group of modal weights for each sample in a self-adaptive manner on the basis of the decoupled modal importance weights; the dynamic attention fusion module performs dynamic weighting and cross-modal attention fusion on the modal specific features by using the modal weight to obtain enhanced specific features; and finally, fusing the enhanced specific features and the enhanced modal sharing features to carry out emotion prediction. According to the method, the defect that an existing dynamic fusion method makes a decision on entanglement features is overcome, and more accurate modal importance evaluation and fusion are achieved.

The invention provides a public opinion thermodynamic statistical method and system based on multi-AI agent collaboration, and the method comprises the steps: collecting public opinion data from a plurality of heterogeneous data sources, and carrying out the cleaning and standardization processing of the public opinion data, and obtaining the standardized public opinion data; identifying public opinion transaction events in the standardized public opinion data; for a public opinion transaction event, constructing a multi-dimensional thermal factor based on a large language model; wherein the multi-dimensional thermal factors comprise a volume factor based on an information propagation map, an emotion intensity factor based on text fine-grained emotion analysis and a source weight factor based on information source influence evaluation; according to the characteristics of the public opinion transaction event, configuring weights of the volume factor, the emotion intensity factor and the source weight factor; performing weighted fusion on the multi-dimensional thermal factors after weight configuration to obtain a comprehensive thermal value; and comparing the comprehensive thermodynamic value with a preset thermodynamic threshold value to obtain the public opinion thermodynamic level, thereby improving the accuracy of public opinion thermodynamic statistics.

The invention relates to the technical field of computers, in particular to a multi-modal emotional tendency analysis method for natural language processing, which comprises the following steps: acquiring multi-modal original data; obtaining each feature vector; obtaining a multi-modal discrimination probability vector according to each feature vector; obtaining an interpretation text sequence according to each obtained feature vector; obtaining an interpretation discrimination probability vector according to the interpretation text sequence; determining an emotional tendency category label according to the interpretation discrimination probability vector; calculating classification loss according to the interpretation discrimination probability vector and the real emotion label; and the consistency loss and the total loss are explained according to calculation. According to the method, a multi-modal fusion sentiment classification main branch based on modal self-adaptive gating is constructed, an explanation branch for generating an explanation text and alignment loss between probability distributions of the explanation branch and the explanation branch are added, and a sentiment judgment result and natural language explanation are restrained at the same time under a unified end-to-end training framework; therefore, the technical problem of multi-modal emotion recognition precision and'interpretation-decision 'consistency is considered.

The invention discloses an intelligent bidding document generation and waste bidding risk confrontation optimization method and system, and relates to the technical field of natural language processing in the power industry. In order to solve the problems of low bid winning rate, high bid rejecting risk and mismatching of text styles in the existing intelligent bidding document in the power industry, the method comprises the following steps of: converting unstructured power bidding and tendering data into a knowledge graph which can be reasoned by a computer; building a bid invitation party preference feature vector model and calculating preference weight distribution based on a bid invitation party holographic image of a knowledge graph and recessive preference mining; self-adaptive generation of a bidding document first draft is carried out based on style features obtained through emotion calculation and semantic features of stealth preferences, and alignment of bidding document intonation and project attributes is realized through calculation of text similarity; carrying out waste bid risk confrontation detection and correction based on anti-fact reasoning; and carrying out confrontation detection according to the waste bid risk, and carrying out automatic correction so as to output a compliance final manuscript. The method is mainly used for generating the intelligent bidding document of the power industry and carrying out the waste bidding risk confrontation test.

The invention relates to a multi-modal sentiment analysis method based on a large language model and quantum computing, and belongs to the technical field of multi-modal feature alignment and fusion. The multi-modal sentiment analysis method comprises the steps of constructing a multi-modal sentiment analysis data set, extracting text features and multi-granularity image sentiment description features by using the large language model and a target detection technology, carrying out multi-level fusion on the image emotion description and the original text to obtain cross-modal potential emotion association information; designing a cross-modal feature fusion mechanism based on a parameterized quantum circuit, and performing efficient fusion on multi-modal features by using quantum superposition and quantum entanglement features to generate unified emotion feature representation; performing sentiment classification based on the sentiment feature representation, and outputting a sentiment analysis result; according to the method, cross-modal potential emotion association can be effectively captured, the fusion efficiency and expression ability of multi-modal features are improved, the method is remarkably superior to the prior art in emotion analysis tasks, and the robustness and accuracy are high.

The invention provides a cross-platform public opinion information collection and analysis system based on deep learning, and belongs to the technical field of public opinion information collection and analysis. A multi-platform data collector is deployed to capture social media data in real time to establish an original cache pool, and preprocessing and metadata extraction are performed on multi-modal data; a multi-modal deep fusion convolution algorithm is adopted to construct a parallel text, image and audio branch network for feature extraction, a cross-modal attention mechanism is adopted to parallelly calculate correlation weights of all modal features, and a gating fusion unit is used to perform efficient nonlinear transformation and information screening to generate a unified semantic vector. Semantic vectors are input into a self-adaptive cross-modal sentiment analysis recognition model, and the technical problem that the requirement for millisecond-level real-time processing of mass public opinion data cannot be met due to the fact that the inference time of a deep model is too long is solved through a collaborative mechanism of dynamic optimization, parallel processing and hierarchical response.