66 results about "Emotional score" patented technology

The invention discloses an emotion detection system based on facial recognition, and relates to the technical field of computer vision and emotion calculation. A video stream time sequence analysis module is used for extracting a facial micro-expression image sequence of continuous frames, a time sequence feature vector containing a micro-expression intensity gradient, an illumination robustness coefficient and a facial action unit cooperation feature is generated, and a multi-mode dynamic sensing module is combined to carry out real-time analysis on an emotion classification probability, voice emotion parameters and physiological signals. And the fusion decision module performs dynamic weighted fusion on the multi-modal data based on the scene adaptive weight, and finally generates a comprehensive emotion score. Through multi-modal time sequence modeling and a dynamic weight optimization mechanism, the accuracy and environmental adaptability of emotion recognition are remarkably improved, and real-time perception and accurate decision making of customer emotion are realized in a target scene.

The invention discloses a VEM-Token vocal music emotion multi-mode token song sound and accompaniment deep learning method, which is different from an existing method that information is segmented into textual token lexical elements by artificial intelligence and then recognition is carried out on the textual token lexical elements. The method comprises the following steps: performing frequency spectrum processing on a vocal music file, detecting rhythm, segmenting the vocal music file subjected to frequency spectrum processing into a VEM-Token sequence according to the vocal music rhythm, establishing a VEM coordinate system, a VEM function and a VEM library according to multiple modes such as lyrics, singing sound, accompaniment, singer emotion, accompaniment emotion, video and image, performing VEM-Token identification, separating a singing sound stream and an accompaniment stream, and determining the vocal music according to a vocal music expert. And performing multi-modal emotion scoring on the vocal music sample, obtaining a VEM parameter by adopting supervised learning and deep learning algorithms, and learning to obtain the multi-modal emotion of the vocal music sample. For other vocal music works, vocal music multi-mode emotions can be recognized, and a lyric score, a VEM-Token song sound score, a VEM-Token accompaniment score and a VEM-Token music score are output. AI systems including a common large model and the like are accessed, and a vocal music agent Agent capable of listening to the singing and identifying the music score is developed.

A system for analyzing audio includes a memory configured to store known digital audio representation containing known fraudulent audio streams and a processor operably coupled to the memory. The processor receives a portion of an audio stream from an external device and produces a transcript of the portion of the audio stream. The processor then determines a timing score, an emotional score, a background score, and a content score by analyzing the portion of an audio stream and the corresponding transcript and comparing them to the known digital audio representations and transcripts. The processor then determines if the audio stream is malicious by combining the timing score, emotional score, background score, and content score to produce a combined score and comparing the combined score to a threshold. The processor notifies a user that the call may be fraudulent when the combined score is greater than the threshold.

The invention discloses an EEG emotion analysis method based on a deep neural network, and the method comprises the steps: collecting multi-channel data, carrying out the multi-dimensional feature extraction of the data, and carrying out the smoothing and standardization processing of the features; inputting the processed features into a CNN-LSTM model, analyzing the features and obtaining emotion features; further analyzing the emotional features and the signal feature dimensions, and outputting accurate emotional scores; according to the method, multi-dimensional feature extraction and emotion quantitative discrimination of all-channel electroencephalogram data are realized, traditional category discrimination is replaced, and the effects of high efficiency, high precision and meticulous effects are achieved.

The invention discloses a forum lever speech recognition method and device based on deep learning. The method comprises the steps of obtaining comment data and performing preprocessing; calculating a text sentiment score based on sentiment words, negative words, adverbs and exclamation marks in the preprocessed comment data; calculating emoticon emotion scores based on emoticons in the comment data; obtaining a final emotion score based on the text emotion score and the expression emotion score; vectorizing and compressing the preprocessed comment data to obtain text sentence vector representation; performing weighted fusion on the text sentence vector representation and the final sentiment score to obtain sentiment value weighted sentence embedding representation; using the sentiment value weighted sentence embedding representation and the corresponding label to train a sentiment classification model; the trained sentiment classification model is used for identifying whether the speech is a bar-sperm speech or not. According to the method, sentiment words, negative words, adverbs, other words, exclamation marks and emoticons are comprehensively considered, and the recognition problem of the lever speech containing hidden contexts is solved.

The invention relates to the technical field of data analysis, and provides a user emotion recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: generating a target emotion change curve according to a pre-obtained historical video call record of a target person, carrying out the trend analysis of the target emotion change curve, and obtaining an emotion change habit; acquiring real-time call information of the target person, and performing dynamic weight division on the real-time call information based on an emotion change habit to obtain an output weight; performing standardization processing on the real-time call information, and performing weighted fusion on the processed real-time call information according to the output weight to obtain a real-time emotion score; and obtaining a current emotion type of the target person according to the real-time emotion score, generating a prompt interface according to the current emotion type, and displaying the prompt interface on a terminal interface. According to the method, the emotion recognition precision, robustness and practical value are remarkably improved, the method can be widely applied to scenes such as video calls and intelligent customer service, and the man-machine interaction experience is improved.

The invention discloses a target-centered semi-supervised fine-grained sentiment analysis method, and belongs to the technical field of deep learning and natural language processing. The method comprises the following steps of: constructing a data set based on social media text data with an emotion label, encoding an input text by using a fusion word vector and a position vector, and enriching context semantic information of each word in the data set; constructing a PWT encoder, a gating classifier and a bidirectional PWT decoder; constructing an emotion analysis network model; performing semi-supervised training on the sentiment analysis network model by using the labeled data to obtain a trained model; and using the trained sentiment analysis network model to carry out sentiment score calculation on the unlabeled data, and outputting a sentiment score. According to the method, automatic public opinion acquisition in a big data era can be researched, and sentiment analysis is carried out from massive texts.

The invention discloses a legal consultation risk scoring method and system fusing emotional intensity and social influence, and belongs to the technical field of artificial intelligence, and the method comprises the steps: constructing an emotional vocabulary library intensity table, and calculating an emotional score of a consultation text in combination with the emotional vocabulary library intensity table; and constructing a social influence strength table, calculating a social influence score of the consultation text in combination with the social influence strength table, and obtaining a risk score of the consultation text based on the emotion score and the social influence score. High-risk consultation can be recognized in an early stage, processing resources are reasonably distributed, potential legal crisis and social events are prevented, meanwhile, the transparency requirement is met, and intelligence of legal services and modernization of social governance are promoted.

The invention relates to the technical field of information management, in particular to an Internet hot news intelligent acquisition and management system, which comprises a news acquisition module, a news management module and a news management module, the news labeling module is used for primarily processing the collected news information, adding labels and verifying the accuracy of the news information; the influence analysis module is used for obtaining the comment number, the like number, the reply number, the sharing frequency and the reading amount corresponding to the news information and evaluating the influence analysis coefficient of the news information; identifying the development trend of the news information according to the change condition of the news information along with time; the emotion analysis module is used for identifying positive, negative and neutral emotions in the news, setting emotion scores for the identified emotions, and identifying emotion influence coefficients corresponding to the news information according to the obtained emotion scores; and the news processing efficiency and accuracy are improved.

The invention discloses a campus bullying early warning method and system based on artificial intelligence, and relates to the technical field of campus psychological health monitoring, and the method comprises the following steps: obtaining a drawing emotion score of a student; the drawing emotion score is obtained by collecting and processing drawing data of a student in a drawing scene; whether the drawing emotion score is larger than a preset threshold value or not is judged, if yes, an interactive dialogue of the drawing scene is generated, and dialogue texts, voice fundamental frequency data of students, eye movement track data and statement complexity data in the interactive dialogue process are collected in real time. According to the scheme, the local emotion region is positioned through semantic segmentation, and the emotion weights of different regions are quantized in combination with the convolutional neural network, so that directional questioning can be performed for specific emotion sources in the question generation process, more real emotion feedback is triggered in subsequent data acquisition of voice fundamental frequency, eye movement tracks and the like, and the accuracy of question generation is improved. And a high-quality multi-modal analysis data basis is provided for campus bullying early warning.

The invention discloses a teaching quality analysis method, system and device based on big data and a medium, and relates to the field of teaching quality evaluation. The method comprises the following steps: adjusting initial difficulty according to the correlation between the content of teaching courseware and a preset key point to obtain first difficulty; obtaining first comprehensive emotion values of all students in each first preset time period from the teaching video, integrating the plurality of first comprehensive emotion values to form a second comprehensive emotion value of the teaching video, and obtaining an emotion score according to the first difficulty and the second comprehensive emotion value; determining a score increase value according to the first score set and the second score set, determining the weight of each student according to the classroom participation degree of each student in a second preset time period, and obtaining a score score according to the weight and the score increase value of each student; and determining the teaching quality in the second preset time period according to the plurality of emotion scores and score scores in the second preset time period. By implementing the technical scheme provided by the invention, the teaching quality can be comprehensively and accurately evaluated.

The invention relates to the technical field of emotion supervision, in particular to a middle school student emotion supervision system based on AI assistance, which comprises a voice acquisition module, a voice text extraction module and an emotion scoring module, then, a voice text extraction module constructs a deep neural network model, voice texts corresponding to the digital signals are extracted through the deep neural network model, emotion vocabularies in the voice texts are extracted again through an emotion scoring module, and different emotion vocabulary sets and corresponding scores are formulated; and the emotion vocabularies in the emotion vocabulary set are associated with the digital signals in the voice acquisition module, so that when the voice text extraction module and the emotion scoring module extract the voice files and the emotion vocabularies, the error and uncertainty when the emotion vocabularies and the voice texts are extracted are reduced.

A method for generating predictions related to a set of input text is provided. Text may be received. A machine learning model may be utilized to determine a set of classification probabilities of the text relative to a set of anchors. An emotional score indicative of emotional content of the text may be determined based at least in part on a convex combination of the set of probabilities for the text. One or more predictions related to the text may be generated.

The invention relates to the technical field of smart home, and discloses an illuminating lamp control method and related device for predicting user emotion based on voice, and the method comprises the steps: collecting a voice signal of a to-be-recognized user through a voice collection module after an illuminating lamp is turned on and enters an intelligent mode, collecting environment data, and obtaining the attribute information of the to-be-recognized user; and inputting the voice signal, the environment data and the attribute information into a user emotion recognition model to obtain an emotion score of the to-be-recognized user, determining a target color temperature and target brightness of the illuminating lamp based on the emotion score, adjusting the color temperature of the illuminating lamp to the target color temperature, and adjusting the brightness of the illuminating lamp to the target brightness. According to the invention, the emotion of the user is recognized according to the speech of the user, the attribute information and the environment where the user is located to control the color temperature and brightness of the illuminating lamp, the illuminating lamp can be adjusted to the light color temperature and brightness matched with the emotion of the user without user intervention, and the intelligent degree of illuminating lamp control is improved.

The present invention relates to a multi-modal emotion recognition method and system for companion robots. The method includes separately collecting facial expression pictures, voice signals, and electroencephalogram signals; extracting emotional feature vectors of facial expressions, emotional feature vectors of voices, and feature vectors of electroencephalogram signals; obtaining a weight matrix, multiplying each feature vector by the weight matrix to obtain a fused feature; classifying four common emotions, namely happiness, sadness, calmness, and disgust, through a support vector machine; and predicting emotional scores by evolving emotions into four dimensions of pleasure-tension-excitement-certainty and adopting a multivariate non-linear regression method. Compared with the prior art, the present invention has a closer emotion recognition ability to humans through information fusion; realizes the ability of autonomous evolution and continuous adjustment of emotion discrimination by using the form of dynamically updating weight parameters; and discrete and continuous emotion recognition can achieve a more scientific and in-depth description of emotional changes.

To provide a customer experience value analysis device capable of grasping how much an emotional value experienced by a customer is changed before and after an event.SOLUTION: The CX analysis device 10 includes a data acquisition unit 101 that acquires text data including information on a specific facility based on social media information posted on a social media platform, an emotion analysis unit 102 that analyzes an emotion of a poster who has posted the social media information based on a predefined emotional score based on the text data, a CX analysis unit 103 that analyzes a customer experience value based on an analysis result of the emotion, and a processed data display unit 104 that outputs a calculation result of a customer experience value analysis score calculated based on an analysis result of the customer experience value.SELECTED DRAWING: Figure 1

Analyzing emotion in a videoconference includes receiving video media stream(s) of a user participating in the videoconference. A face of the user is detected in frame(s) of the video media stream(s). An emotional state of the user is classified. In one or more embodiments, an emotional score for the user is assigned and visualized on a display. In one or more embodiments, additional video media stream(s) of additional user(s) participating in the videoconference are also received, corresponding face(s) of the additional user(s) are also detected, and corresponding emotional state(s) of the additional user(s) are also classified. In one or more embodiments, emotional score(s) for the additional user(s) are also assigned and visualized on the display, together with the emotional score for the user. Additionally, or alternatively, a combined emotional score for the user and the additional user(s) may be assigned and visualized on the display.

The invention discloses a public opinion management method based on evidence reasoning, a product, a medium and equipment, and relates to the technical field of data mining and analysis, and the method comprises the steps: obtaining original public opinion data of a plurality of data sources; preprocessing the original public opinion data to obtain preprocessed public opinion data; performing emotional tendency scoring on the preprocessed public opinion data by adopting an emotional analysis model and a theme generation model to obtain an emotional score; based on the emotion score, a Dempster-Shafer evidence theory is adopted to obtain a comprehensive emotion score; and performing public opinion state analysis and response strategy formulation according to the comprehensive emotion score to complete public opinion management. The public opinion management efficiency and accuracy can be effectively improved.

The invention discloses an emotion intervention method and device, a vehicle and a storage medium. The method comprises the steps that video data and audio data of a driver and passengers in the vehicle driving process are acquired; performing emotion state detection on the driver according to the video data and the audio data to obtain an emotion score of the driver; when it is determined that the driver and the passenger are in a negative emotional state according to the emotional score, an intervention strategy is determined according to the emotional score, and the intervention strategy is used for smoothing the negative emotion of the driver and the passenger; and performing emotion intervention on the driver and passengers according to the intervention strategy. According to the method, when it is determined that the driver is in the negative emotional state based on the detected emotional score of the driver in the vehicle driving process, emotional intervention is conducted on the driver according to the emotional score so as to flatten the negative emotions of the driver, the situation that the driver is distracted to drive, and consequently the driving risk of vehicle driving is increased can be avoided, and the driving safety is improved. And the driving safety of vehicle driving can be improved.

The invention provides a product evaluation method and system and a storage medium. The method comprises the following steps: constructing a score matrix according to a product sample image and an expert score; determining key emotion vocabularies in the emotion sample vocabularies according to the scoring matrix, and constructing model training data according to the key emotion vocabularies and the model sample images; performing model training on the product evaluation model according to the model training data until the product evaluation model converges; and inputting a to-be-evaluated product image into the converged product evaluation model for emotion evaluation to obtain an emotion score, and generating a product evaluation result according to the emotion score. According to the embodiment of the invention, model training is carried out on the product evaluation model through the model training data, so that the converged product evaluation model can accurately capture emotion preferences of the user on different product images, emotion evaluation can be effectively carried out on the input to-be-evaluated product image, an emotion score is obtained, and the user experience is improved. And a product evaluation result can be automatically generated based on the emotion score.

A method for assisting a patient through a therapy session using a live real-time voice interaction with a narrative audio is disclosed. The method includes steps of: extracting an emotional content from the set of multimedia in a playlist; calculating an emotional score for each emotional content in the playlist using a deep machine learning model; generating an emotional arc for the emotional content based on the calculated emotional score, previous response from the patient to different genres and personal information of the patient; generating a narrative text from the set of multimedia and generating a narrative text story based on the generated narrative text and the personal information of the patient using an artificial intelligence (AI) story model; converting the generated narrative text story to an audio; and generating a narrative audio by combining the audio file with the emotional arc.

We provide the system. [Solution] A natural language processing method for analyzing communication data received from a user, An evaluation method for assigning an emotional score to the analyzed communication data, A selection means that filters communications based on an emotional rating value derived from user settings, A means of providing filtered communications to the user terminal, A learning method that improves filtering accuracy by updating the machine learning model based on adjustments to user selection settings, A receiving means that can identify and display communications that point out new areas for improvement, A system that includes this.

The invention discloses a market quotation prediction method and system based on block chain data and multi-modal fusion, and relates to the technical field of data analysis, by fusing block chain native data, an objective physical state reference is established from a market microstructure level, and the depth and uniqueness of analysis are enhanced; and by capturing and analyzing the original sequential logic triggered by the emotion instead of simple emotion scores, the key insight in the market psychological evolution rhythm is reserved. According to the method, the joint anomaly is judged only when the physical state deviates from the standard and the emotion triggering sequence is abnormal at the same time, the recognition specificity of a real inflection point signal is remarkably improved, noise interference caused by fluctuation of a single factor is effectively filtered, and therefore the reliability of a prediction result is improved.

The application provides a regional data monitoring method and system, wherein the method comprises the following steps: performing semantic extraction on a financial text to obtain an initial semantic vector of each segmented word in the financial text; determining an emotional segmented word and an emotional score of the emotional segmented word in the financial text based on an emotional dictionary, and determining a trend expression word and a trend score of the trend expression word in the financial text based on a trend expression dictionary; strengthening the initial semantic vector of each emotional segmented word based on the emotional score of the emotional segmented word, and strengthening the initial semantic vector of each trend expression word based on the trend score of the trend expression word, to obtain a strengthened semantic vector of the emotional segmented word and the trend expression word; performing regression analysis on the strengthened semantic vector of the emotional segmented word and the trend expression word and the initial semantic vector of the remaining segmented word in the financial text to obtain an emotional score of the financial text; and based on the emotional score of the financial text, statistical target regional financial public opinion data. The application realizes more accurate public opinion data monitoring of a target region.

The application relates to the technical field of data processing, in particular to a communication equipment service quality dynamic evaluation and optimization system based on customer follow-up visits, which comprises an acquisition module, an abnormality judgment module, a cause analysis module, a feature calculation module, a quality evaluation module, an output module and an adjustment module. The application identifies unexpressed attitudes according to the time sequence entropy value of the emotional score sequence; quantifies the deviation degree of explicit expression and implicit emotion through the KL divergence of IVR keys and voice emotion; cross-verification of the follow-up text semantics and the time consumption of the maintenance process is carried out to realize cause tracing; the information content behind the silence is quantified, comprehensive judgment is realized through the calculation of the quality deviation degree; the threshold is dynamically adjusted according to the fluctuation characteristics of the deviation degree to form a closed loop, and the problems of low real experience recognition accuracy and low service quality abnormality cause detection accuracy caused by excessive dependence on single satisfaction score and the inability to recognize complex emotional guidance are effectively solved.

The invention discloses a data method for depressive state monitoring, which comprises the following steps: acquiring personal basic information of a depressive disorder tester, and matching corresponding test content data based on the personal basic information; obtaining response content data of the depression tester, extracting first response emotion feature data, a corresponding emotion type and an emotion score based on a preset deep learning model, and obtaining second response emotion feature data, a corresponding emotion type and an emotion score based on a preset emotion driving model; establishing an emotion matrix of the depression testers according to the emotion types and the emotion scores, and calling a depression emotion matrix in a depression database, and calculating a first depression state evaluation value and a second depression state evaluation value corresponding to the first emotion matrix, the second emotion matrix and the depression emotion matrix, comparing the first depression state evaluation value and the second depression state evaluation value with a preset depression emotion threshold value to obtain a depression comparison result, and judging the mental health state of the depression tester according to the depression comparison result.

The present application relates to the field of computer technology, and is a speech recognition solution, specifically a multimodal interpretation training evaluation method and evaluation device. The embodiment provided by the present invention determines the semantic correctness and emotional tendency of the translation by performing standard text matching and emotional tendency score calculation on the text data respectively, extracts multiple features from the audio data and determines the translation fluency based on the expression of multiple features, and determines the corresponding semantic score, emotional score and fluency score according to the semantic correctness, emotional tendency and translation fluency, and evaluates the trainee's translation behavior according to the semantic score, emotional score and fluency score. The method provided in the embodiment of the present application can realize the automated evaluation of the translation process and results based on natural language processing technology and speech processing technology, and can comprehensively evaluate the results from three aspects: accurate semantic expression, emotional tendency and fluency expression compared with the existing technology.

The invention relates to the technical field of emotion calculation, neuroscience and man-machine interaction crossing, in particular to an emotion calculation-oriented man-machine interaction multi-scale neural mechanism interpretation method, which comprises the following steps of: (1) in a man-machine interaction process, synchronously acquiring multi-channel electroencephalogram and electrocardio physiological data, and acquiring a multi-dimensional subjective emotion score as a label; (2) calculating dynamic function connection between brain intervals, and quantifying a brain collaborative activity mode; meanwhile, heart rate variability characteristics are extracted, and autonomic nervous system response is quantified; and (3) establishing a mapping relation from multi-scale physiological features to multi-dimensional emotion tags by adopting a partial least squares regression model, and identifying key heart-brain interaction indexes through variable projection importance analysis and load analysis so as to realize visualization and deep interpretation of an emotional neural mechanism. According to the method, the core challenges of difficulty in emotion state decoding and opaque internal biological mechanism of an existing emotion calculation technology in a complex human-computer interaction scene can be effectively solved.

The disclosed embodiments disclose a method, device, medium, and product for creating a word cloud based on natural language processing. The method includes: preprocessing a text to obtain a plurality of valid words; obtaining the frequency of occurrence of the valid words in the text; performing sentiment analysis on the valid words to obtain the sentiment score of the valid words; obtaining the relevance of the valid words to the theme of the text; screening out keywords from the valid words based on the frequency of occurrence, the sentiment score, and the relevance; and generating a word cloud based on the keywords. The method can generate a word cloud that effectively reflects the key points, sentimental tendencies, and themes of the text content by combining the frequency of occurrence, sentiment score, and relevance, thereby improving the interpretability of the word cloud and making it easier for the audience to understand and absorb the text information.

The embodiment of the invention discloses a character emotion recognition method and device. The method comprises the steps of performing emotion recognition on audio of a target person, and determining a target emotion type and a corresponding first confidence coefficient; performing key point collision on the face key points in the monitoring picture and the emotion key points; if it is determined that the monitoring picture is suitable for identifying the target emotion type of the target person, capturing the target person, and determining an image emotion score; and determining a character emotion recognition result of the target character according to the first confidence corresponding to the target emotion type and the image emotion score. According to the scheme, based on the key point collision of the face key points and the character face emotion key points in the monitoring picture, image snapshot is carried out when the picture which visually reflects the character emotion is presented in the monitoring picture, the reference of the snapshot image to emotion recognition is improved, and through combined recognition of the audio and the snapshot image, the recognition efficiency is improved. And the emotion recognition accuracy is improved.