1154 results about "Sentiment analysis" patented technology

In one aspect, the invention provides apparatuses and methods for determining the sentiment expressed in answers to survey questions. Advantageously, the sentiment may be automatically determined using natural language processing. In another aspect, the invention provides apparatuses and methods for analyzing the sentiment of survey respondents and presenting the information as actionable data.

A computer system performs financial analysis on one or more financial entities, which may be corporations, securities, etc., based on the sentiment expressed about the one or more financial entities within raw textual data stored in one or more electronic data sources containing information or text related to one or more financial entities. The computer system includes a content mining search agent that identifies one or more words or phrases within raw textual data in the data sources using natural language processing to identify relevant raw textual data related to the one or more financial entities, a sentiment analyzer that analyzes the relevant raw textual data to determine the nature or the strength of the sentiment expressed about the one or more financial entities within the relevant raw textual data and that assigns a value to the nature or strength of the sentiment expressed about the one or more financial entities within the relevant raw textual data, and a user interface program that controls the content mining search agent and the sentiment analyzer and that displays, to a user, the values of the nature or strength of the sentiment expressed about the one or more financial entities within the data sources. This computer system enables a user to make better decisions regarding whether or not to purchase or invest in the one or more financial entities.

The invention discloses an attention CNNs and CCR-based text sentiment analysis method and belongs to the field of natural language processing. The method comprises the following steps of 1, training a semantic word vector and a sentiment word vector by utilizing original text data and performing dictionary word vector establishment by utilizing a collected sentiment dictionary; 2, capturing context semantics of words by utilizing a long-short-term memory (LSTM) network to eliminate ambiguity; 3, extracting local features of a text in combination with convolution kernels with different filtering lengths by utilizing a convolutional neural network; 4, extracting global features by utilizing three different attention mechanisms; 5, performing artificial feature extraction on the original text data; 6, training a multimodal uniform regression target function by utilizing the local features, the global features and artificial features; and 7, performing sentiment polarity prediction by utilizing a multimodal uniform regression prediction method. Compared with a method adopting a single word vector, a method only extracting the local features of the text, or the like, the text sentiment analysis method can further improve the sentiment classification precision.

The invention discloses a sentiment analysis method oriented to a micro-blog short text. The method comprises the following steps: step 1, collecting micro-blog data including keywords so as to store in a database; step 2, pre-processing the micro-blog data; step 3, loading associated dictionaries; step 4, processing sentence division and filtering sentences which do not include user configuration keywords; step 5, processing word division to the sentences including the keywords and labeling parts of speech; step 6, processing dependency sentence structure analysis to the sentences including subjects by a sentence structure analyzing tool; step 7, judging the polarity of each sentence including subject words; and step 8, judging the polarity of a whole micro-blog after judging the polarities of all sentences including the subject words. According to the sentiment analysis method provided by the invention, sentiment analysis is more specific, so that users can know sentiment attitude of concerned aspects from the micro-blog.

A computer implemented method and apparatus for acquiring sentiment analysis information based on user reactions to displayable content. The method comprises receiving reaction data of at least one individual user viewing portions of displayable content, wherein the reaction data of each user includes indications representative of at least one of a time-varying emotional state of the user and a time-varying level of interest of the user captured during viewing of the displayable content; and performing sentiment analysis on the reaction data by at least one of: categorizing portions of the displayable content based on one of the reaction of one user or aggregated reactions of a plurality of users, and identifying at least one portion of the displayable content having one of a more favorable and a less favorable reaction by the at least one user, by comparison to one of a baseline and reactions to other portions of the displayable content.

Users receive information which must be filtered, processed, analysed, reviewed, consolidated and distributed or acted upon. Prior art tools automatically processing content to assign sentiment to the content are ineffective as essential aspects such as context are not considered. Embodiments of the invention provide automatic contextual based sentiment classification of content in terms of both sentiments expressed and their intensity. Further a content set is analysed to rapidly establish an “at-a-glance” type assessment of the key topics/themes present within the content set and sentimentally annotate each. Importantly embodiments of the invention also provide for a user to establish the basis for the sentiment associated with an item of or set of content, i.e. make it explainable. Further embodiments of the invention provide for the establishment of psychological tone to sentiments where the sentiments and psychological tones to be tuned from the context or domain of the content.

A method for determining a sentiment associated with an entity includes inputting a plurality of texts associated with the entity, labeling seed words in the plurality of texts as positive or negative, determining a score estimate for the plurality of words based on the labeling, re-enumerating paths of the plurality of words and determining a number of sentiment alternations, determining a final score for the plurality of words using only paths whose number of alternations is within a threshold, converting the final scores to corresponding z-scores for each of the plurality of words, and outputting the sentiment associated with the entity.

A set of data pertaining to artists and other content creators is retrieved periodically from various Internet-connected sources, the data being characterized as both quantitative and qualitative data. The data is analyzed through various means, including sentiment analysis, to produce quantitative information, including a score or set of scores, that is descriptive of an artist's popularity as well as the ranking of an artist among other artists within a category or set of categories.

The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

The invention provides a Chinese sentiment analysis method based on BERT, LSTM and CNN fusion. The Chinese sentiment analysis method comprises the steps: performing text preprocessing on a plurality of Chinese corpora in a Chinese corpus data set to obtain a plurality of sequences corresponding to the plurality of Chinese corpora; extracting word embedding of each sequence by using a BERT model; performing feature extraction on each sequence by adopting BERT, LSTM and CNN to obtain text deep semantic features corresponding to each sequence; and classifying the obtained text deep semantic features by using a softmax classifier to train and test the model so as to realize sentiment polarity prediction analysis. According to the technology, the defects in the prior art can be overcome, and the accuracy of Chinese text sentiment analysis is improved.

A real-time, stream data information integration and analytics system can include an information engine that performs real-time entity extraction to create key-value pairs of attributes for a personal profile and integrates similar personal profiles generated from same or different data sources into a single person-of-interest profile. The real-time, stream data information integration and analytics system can further include a real-time analytics module that performs a variety of analytics using the person-of-interest profiles and updates the person-of-interest profiles with scores and other results of the variety of analytics. The variety of real-time analytics can include sentiment analysis and at least some aspects of threat detection analysis. The sentiment analysis can be used to provide a recommender system and the threat detection analysis can be used to identify and predict threats and opportunities.

A method and system for text data analysis by performing deep syntactic and semantic analysis of text data and extracting entities and facts from the text data based on the results of deep syntactic and semantic analysis, including extraction of sentiments using a sentiment lexicon constructed upon a semantic hierarchy. The data analysis can include determining sign of the extracted sentiment, aggregate function of the text data, analyzing social mood, and classifying the text data.

The invention discloses a natural language processing-based multi-language analysis method and device. The method comprises the following steps of: selecting to input a natural language text information language category through a language detection training model; obtaining word embedding expression information of corresponding words which can be recognized by a computer through a trained word vector model, and extracting a keyword of the obtained word embedding expression information through a TF-IDF manner; calculating an article vector and a category vector of each preset category according to the keyword and a keyword weight, and calculating a similarity between an article of natural language text information and each preset category so as to determine a text classification result ofthe natural language text information; and inputting the word embedding expression information of the natural language text information into a trained convolutional neural network and a parallel-framework text emotion analysis model of a bidirectional gate circulation unit, and obtaining a final emotion tendency value through calculation. According to the method and device, the problem that traditional multi-language analysis method needs to know domain knowledges of related linguistics and needs plenty of manpower to carry out operation is solved.

The invention provides a customer service information providing method and device, electronic equipment and a storage medium. The method comprises the steps of receiving a Chinese text input by a user; inputting the input Chinese text into a Chinese customer service question-answering model based on a Bi-LSTM (Bidirectional Long Short-Term Memory) model and a CNN (Convolutional Neural Network) model to acquire an answering statement; inputting the input Chinese text into a content extraction and intention classification model based on a Bi-LSTM-CRF (Conditional Random Field) model and an LSTMclassifier to acquire customer intention classification and key information; determining service recommended to a user according to the customer intention classification and the key information; inputting the input Chinese text into a Chinese text emotion analysis model based on the CNN model to acquire a user emotion classification; adjusting the answering statement according to the user emotionclassification; and in combination with the adjusted answering statement and the determined service, providing customer service information to the user. According to the method and device optimizationmodel provided by the invention, the automatic customer service answering is realized.

The invention discloses a model training method for cross-domain sentiment analysis based on a convolutional neural network, belongs to the field of cross-domain sentiment classification, and aims tosolve the problem of cross-domain sentiment analysis. The method comprises: S1, preprocessing a text; S2, training a word vector model; S3, performing cross-domain model migration; wherein the step S3is carried out in sequence; a neural network model is trained through a source domain; migrating the trained model. Firstly, a weight value of a convolution kernel in a model is shared, a convolutionkernel weight trained in a source domain is used for extracting corresponding features in a target domain, a small part of data in the target domain is trained again, parameters of a full connectionlayer weight of the previously trained model are adjusted, and the effect is that model migration is conducted on a cross-domain emotion text.

The invention relates to a text sentiment analysis method and device, a storage medium and computer equipment. A sentence vector in a sentence in a test text is obtained and is formed in a way that the word vectors of words in the sentence are connected, and the sentence vector is independently input into two preset convolutional neural networks and one two-way long short-term memory neural network model to be preprocessed to obtain three sentence feature vectors of the sentence. Three sentence feature vectors are connected, the connected sentence feature vectors are classified through a classifier SVM (Support Vector Machine) to obtain the sentiment classification result of the sentence, and the emotional tendency of a test text can be obtained according to the sentiment classification result of the sentence. By use of the method, the convolutional neural network can be combined to effectively extract local features, the two-way long short-term memory neural network can effectively analyze the advantages of time sequence features, the test text is subjected to the sentiment analysis through the method to obtain robustness with higher emotional tendency and generalization ability,and efficiency is higher.

The invention relates to an industry comment data fine grain sentiment analysis method. The industry comment data fine grain sentiment analysis method is applied to Internet data analysis and comprises obtaining comment data of e-commerce industry goods and preprocessing the comment data; establishing initial industry sentiment word libraries and computing distribution of words under different sentiment polarities through 1-gram and 2-gram; performing Chinese word segmentation on the comment data; based on the sentiment word libraries established through the 1-gram and the 2-gram, utilizing combined sentiment models to perform word modeling to obtain the probability distribution of the words which belong to different topics under different sentiment distributions; utilizing context information to re-determine the sentiment alignment of sentiment words in sentences; performing named entity identification and extracting comment characteristics through conditional random fields to compute the sentiment alignment of comment words of the comment characteristics. The industry comment data fine grain sentiment analysis method computes the sentiment of the comment words through the two dimensions of topic and sentiment to achieve fine grain sentiment analysis on the industry comment data, thereby achieving high precision and interpretability of analysis results.

The invention provides a text classification method and device based on transfer learning, and the method comprises the steps: S1, employing an unlabeled text to train a BERT model, and obtaining a pre-trained word representation BERT model; S2, filtering links, forwarding symbols and user name contents in the to-be-classified text; S3, inputting the filtered text into the word representation BERTmodel trained in the step S1 to obtain a semantic file of the text; and S4, inputting the semantic file of the text into a convolutional neural network for processing to obtain a category label of the sentence in the semantic file. According to the method, transfer learning is used for text classification, and a BERT word representation model trained based on large-scale unlabeled corpora is provided. The word representation model has universality, does not depend on the specific text field, and can also be used for other tasks such as entity extraction and sentiment analysis.

The invention relates to the technical field of artificial intelligence, and discloses a text sentiment analysis method, a text sentiment analysis system and a computer readable storage medium, for improving the accuracy of text sentiment analysis. The method comprises the steps of inputting a word vector corresponding to any sentence into a preset LSTM network model, thus acquiring a hiding layervector of each word; tagging the part-of-speech of an acquired text word set, training the text word set carrying part-of-speech tagging information, and splitting a part-of-speech vector matrix generated by training by using words as units, thus acquiring the part-of-speech vector corresponding to each word; using sentences as units, performing word embedding weighted summation attention analysis on the hiding layer vector and the part-of-speech vector corresponding to each word in the sentence to acquire a sentence vector carrying attention information of each sentence, and using the sentence vector carrying the attention information to serve as the input of a sentiment classification model, thus acquiring a sentiment classification result of each sentence and/or a classification resultof the original text.

The invention belongs to the technical field of the natural language processing of computers, in particular to a construction and utilization method for a context-aware dynamic word or character vector on the basis of deep learning. The dynamic construction method for the context-aware dynamic word or character vector on the basis of the deep learning comprises the following steps of: in massive texts, through an unsupervised learning method, simultaneously learning a global feature vector of a word or character and the feature vector representation of the global feature vector when a specific context appears, and combining the global feature vector with the context feature vector, and dynamically generating word or character vector representation. By use of the method, the word or character vector dynamically constructed on the basis of the context can be applied to a natural language processing system. The method is mainly used for solving a problem that the word or character vector expresses different meanings in different contexts, i.e. the problem that one word or one character has multiple meanings can be solved. The dynamic word or character vector can be used for obviously improving the performance of various natural language processing tasks of different languages, wherein the tasks comprise Chinese word segmentation, part-of-speech tagging, naming recognition, grammatical analysis, semantic role tagging, sentiment analysis, text classification, machine translation and the like.

Data is received from multiple data sources. At least one of the data sources is an active audio or video communication. The received data is analyzed by extracting instances of a keyword from the received data and analyzing contextual data near the keyword. Sentiment about the extracted keyword is gauged based on the contextual data. The derived sentiment data from the multiple data sources is aggregated, and an aggregated view of the derived sentiment data is presented.

The invention relates to a bidirectional LSTM model emotion analysis method based on attention enhancement. According to the method, an attention mechanism and a bidirectional LSTM model are combined;text semantic information is learned by using a bidirectional LSTM model; the attention mechanism is used for enhancing attention to key words, the method comprises the steps that firstly, input sentences are expressed through pre-trained word vectors, then expression is conducted through a bidirectional LSTM model and attention model learning, the vectors expressed through the two parts are spliced, and finally text sentiment analysis work is completed through a classifier. According to the method, the semantic information of the text is learned by using bidirectional LSTM; a self-attentionmechanism established on a word vector is used for enhancing the attention degree of emotion keywords in the sentence; A word vector attention mechanism and a bidirectional LSTM are of a parallel structure, experiments show that the model provided by the invention shows superior performance, and exceeds a known best model on a plurality of indexes, so that the requirements of practical applicationcan be well met.

The invention comprises a bimodal man-man conversation sentiment analysis system and a bimodal man-man conversation sentiment analysis method based on machine learning. The bimodal man-man conversation sentiment analysis system is characterized by comprising a speech recognition module, a text deep-layer feature extraction module, a speech segmentation module, an acoustic feature extraction module, a feature fusion module and an sentiment analysis module, wherein the speech recognition module is used for recognizing speech content and a time label; the text deep-layer feature extraction module is used for completing the extraction of text deep-layer word level features and text deep-layer sentence level features; the speech segmentation module is used for segmenting single sentence speech from entire speech; the acoustic feature extraction module is used for completing the extraction of acoustic features of the speech; the feature fusion module is used for fusing the obtained text deep-layer features with the acoustic features; and the sentiment analysis module is used for acquiring sentiment polarities of the speech to be subjected to sentiment analysis. The bimodal man-man conversation sentiment analysis method can integrate the text and audio modals for recognizing conversation sentiment, and fully utilizes features of word vectors and sentence vectors, thereby improving the precision of recognition.

The sentiment engine includes a sentiment module configured to gather opinions or determine sentiment expressed in documents, a crawling module configured to crawl servers to obtain at least a subset of the documents or opinions from social media websites, a keyword module configured to extract keywords from documents, a filtering module configured to filter keywords and documents, and a classification module configured to classify documents, sentences, and/or keywords, a polarity prediction module configured to predict the polarity of a sentiment sentence, and a social media net promoter score (SNPS) configured to calculate a loyalty metric of users from social media websites. The functionality of these modules may be combined with one another or in addition to other modules.