632 results about "Emotion classification" patented technology

A method for sentiment classification of a text document using high-order n-grams utilizes a multilevel embedding strategy to project n-grams into a low-dimensional latent semantic space where the projection parameters are trained in a supervised fashion together with the sentiment classification task. Using, for example, a deep convolutional neural network, the semantic embedding of n-grams, the bag-of-occurrence representation of text from n-grams, and the classification function from each review to the sentiment class are learned jointly in one unified discriminative framework.

The invention discloses a subjective text emotion analysis method based on deep learning. The method includes the steps that 1, a C&W-SP model is established based on a C&W model, an emotion label and a word class label of a sentence are labeled in the sentence, a training set of a C&W_SPC&W-SP model is established, a C&W_SP model is trained through the training set, a word vector of each word in the training set is obtained, and a word vector file is formed; 2, a sentence vector set is established through an LSTM model according to the obtained word vector file; 3, a neutral network model is trained through the sentence vector set, and an emotion classification model is obtained; 4, the tested comment sentence is preprocessed, the tested sentence vectors are input in the emotion classification model, and the emotion tendency of the section of comment is obtained through calculation. According to the method, emotion tendency information and word class information are added into words, and the accuracy of emotion analysis is improved.

The invention discloses a comment text emotion classification model training and emotion classification method and device and equipment and belongs to the field of text emotion classification in natural language processing. Model training comprises the steps that a comment text and associated subject and object information are acquired; a comment subject and object attention mechanism is fused based on a first-layer Bi-LSTM network to extract sentence-level feature representation; the comment subject and object attention mechanism is fused based on a second-layer Bi-LSTM network to extract document-level feature representation; and a hyperbolic tangent non-linear mapping function is adopted to map document-level features to an emotion category space, softmax classification is adopted to train parameters in a model, and an optimal text emotion classification model is obtained. According to the method, the hierarchical bidirectional Bi-LSTM network model and the attention mechanism are adopted, context semantic robust perception and semantic expression of the text can be realized, the robustness of text emotion classification can be remarkably improved, and the correct rate of classification is increased.

The invention discloses a text sentiment classification algorithm based on a convolutional neural network and an attention mechanism. The text sentiment classification algorithm comprises the steps of1, establishing the convolutional neural network comprising multiple convolutions and multiple kinds of pooling, and using sentiment classification text for training to obtain a first model; 2, establishing the multi-head point product attention mechanism into which residual connection and nonlinearity are added, and using the sentiment classification text for training to obtain a second model; 3, conducting model fusion on the two models to obtain sentiment classification of the text. Multiple granularity, the convolutions and multiple kinds of pooling are fused into the convolutional neuralnetwork, the residual connection and the nonlinearity are introduced into the attention mechanism, and attention is calculated several times to obtain two text sentiment classification models. Through a Bagging model fusion method, a fusion model is obtained, the text is classified, the advantages that the convolutional neural network can well capture local features and the attention mechanism can well capture global information can be combined, and the more comprehensive text sentiment classification models are obtained.

The invention discloses an aspect-level emotion classification model and method based on dual-memory attention, belonging to the technical field of text emotion classification. The model of the invention mainly comprises three modules: an encoder composed of a standard GRU loop neural network, a GRU loop neural network decoder introducing a feedforward neural network attention layer and a Softmaxclassifier. The model treats input statements as a sequence, based on the attention paid to the position of the aspect-level words in the sentence, Two memory modules are constructed from the originaltext sequence and the hidden layer state of the encoder respectively. The randomly initialized attention distribution is fine-tuned through the attention layer of the feedforward neural network to capture the important emotional features in the sentences, and the encoder-decoder classification model is established based on the learning ability of the GRU loop neural network to the sequence to achieve aspect-level affective classification capabilities. The invention can remarkably improve the robustness of the text emotion classification and improve the classification accuracy.

The invention provides a specific target emotion classification method based on attention coding and a graph convolution network, and the method comprises the steps: obtaining a context and a hidden state vector corresponding to a specific target through a preset bidirectional recurrent neural network model, and carrying out the multi-head self-attention coding of the context and the hidden statevector; extracting a syntax vector in a syntax dependency tree corresponding to the context by combining a point-by-point convolution graph convolutional neural network, and performing multi-head self-attention coding on the syntax vector; then, multi-head interaction attention is used for carrying out interaction fusion on syntactic information codes, context semantic information codes, syntacticinformation codes and specific target semantic information codes; and splicing the fused result with the context semantic information code to obtain a final feature representation, and obtaining an emotion classification result of the specific target based on the feature representation. Compared with the prior art, the relation between the context and the syntax information and the relation between the specific target and the syntax information are fully considered, and the accuracy of sentiment classification is improved.

The invention relates to a convolution-neural-network-based text emotion classification method. The method comprises: a text linguistic data set is collected and data in a text are expressed into one sentence; pretreatment is carried out on the collected text linguistic data set and emotion text linguistic data are classified into training set linguistic data and testing set linguistic data; training is carried out on the text linguistic data set after pretreatment by using a word2vec tool to obtain a word vector model and a text vector is also obtained; the text vector of the training set linguistic data is inputted into a convolution neural network and training is carried out to obtain an emotion classification model; and the text vector of the testing set linguistic data is inputted into the convolution neural network, emotion type classification is carried out on the trained emotion classification model, and an accurate rate of emotion classification is calculated. Therefore, a problem that lots of artificial marks are needed during the previous classification process can be solved.

The invention provides a sentiment classification method for generating a model deep-convinced-degree network on the basis of the probability of depth study. According to the technical scheme of the method, a plurality of Boltzmann machine layers are stacked, namely, output of this layer is used as input of the next layer. By the adoption of the mode, input information can be expressed in a grading mode, and abstraction can be conducted layer by layer. A multi-layer sensor containing a plurality of hidden layers is the basic study structure of the method. More abstract high layers are formed through combining the characteristics of lower layers and are used for expressing attribute categories or characteristics, so that the distribution type character presentation of data can be discovered. The method belongs to monitoring-free study, and a mainly-used model is the deep-convinced-degree network. The method enables a machine to conduct characteristic abstract better so as to improve the accuracy of sentiment classifications.

The invention discloses a commodity target word oriented emotional tendency analysis method, which belongs to the field of the analysis processing of online shopping commodity reviews. The method comprises the following four steps that: 1: corpus preprocessing: carrying out word segmentation on a dataset, and converting a category label into a vector form according to a category number; 2: word vector training: training review data subjected to the word segmentation through a CBOW (Continuous Bag-of-Words Model) to obtain a word vector; 3: adopting a neural network structure, and using an LSTM(Long Short Term Memory) network model structure to enable the network to pay attention to whole-sentence contents; and 4: review sentence emotion classification: taking the output of the neural network as the input of a Softmax function to obtain a final result. By use of the method, semantic description in a semantic space is more accurate, the data is trained through the neural network so as to optimize the weight and the offset parameter in the neural network, parameters trained after continuous iteration make a loss value minimum, at the time, the trained parameters are used for traininga test set, and therefore, higher accuracy can be obtained.

The invention provides an emotion classification model training and textual emotion polarity analysis method and system. The emotion classification model training method comprises the steps that data are acquired from a corpus so that original data are obtained; the original data are preprocessed so that preprocessed data are obtained; word vectors are extracted from the preprocessed data through a neural network model; the word vectors are fused according to preset fusion rules so that sentence vector characteristics are generated; and an emotion classification model is trained according to the sentence vector characteristics so that the trained emotion classification model is obtained. The neural network model is adopted, the words are expressed by low-dimensional spatial vectors, the low-dimensional spatial word vectors are fused into the sentence vector characteristics according to the preset rules, and the emotion classification model is obtained by certain learning models through training so that word vector dimension can be effectively reduced, the dimensions disaster problem can be avoided, correlative attributes between the words can be mined and vector semantic accuracy can be enhanced.

The invention relates to an emotion classification method and system, a storage medium and equipment. The method comprises: encoding a context by using a position word vector and multi-head self-attention; encoding the target word by using a bidirectional gating circulation unit and multi-head self-attention; fully extracting semantic information and position information of long and short sentences; meanwhile, interactively splicing the context semantic information and the target word semantic information for low-order fusion; performing position coding on the basis of low-order fusion by using a capsule network; and performing high-order fusion on the information after low-order fusion by using a multi-head interaction attention mechanism, averagely pooling the high-order fusion information, and splicing the averagely pooled high-order fusion information with the averagely pooled target word semantic information and the averagely pooled context semantic information to obtain target feature representation. Compared with the prior art, the context semantic information, the target word semantic information and the position information are fully fused, and the emotion classification accuracy and efficiency are improved.

The invention discloses a neural network based analyzing method for recognizing emotional tendency of text comments, and belongs to the technical field of computer language and work processing. The method is characterized in that the text comment data are processed through CBOW; each sentence is accurately divided into words or terms, and each sentence is provided with a corresponding class label; the emotional tendency of the comments is determined through a long-and-short term memory LSTM model; the label of each sentence is obtained and compared with a true label to obtain the accuracy rate; and a neural network model is trained to obtain the optimal accuracy rate, thus achieving the purpose of recognizing the emotional tendency of the text comments based on the neural network. According to the method, the GPU is utilized to accelerate the neural network training process, so that the emotion classing accuracy rate is increased, and moreover, the mass corpus data training speed is increased; the emotional tendency of the comments can be effectively recognized; and the method particularly has a good application prospect on e-commerce, film and other fields.

The invention provides a method of micro facial expression detection based on a facial action coding system (FACS). The method has a main content of a visual CNN filter, network architecture and training, migration learning and micro facial expression detection. The method comprises steps: firstly, a robust emotion classification framework is built; the provided network learning model is analyzed; the provided network training filter is visualized in different emotion classification tasks; and the model is applied to micro facial expression detection. The recognition rate of the existing method in micro facial expression detection is improved, the strong correlation between features generated by an unsupervised learning process and action units for a facial expression analysis method is presented, the FACS-based function generalization ability in aspects of providing high-precision score cross data and cross mission is verified, the micro facial expression detection recognition rate is improved, the facial expression can be recognized more accurately, the emotion state is deduced, the effectiveness and the accuracy of application in various fields are improved, and development of artificial intelligence is pushed.

The invention discloses a comment entity-based aspect-level emotion classification method and device and model training thereof. The model training comprises the steps of obtaining a training text comprising comment texts, different entities associated with the comment texts, aspect information and emotion information; Converting words, entities and aspects of the training text into word vector representations; combiing and representing comments in the corresponding entities and aspects based on the first interaction layer; Endowing words at different positions with different weights based onthe second position attention layer; extracting Basic words and syntactic features based on the third-layer LSTM network and the fourth-layer linear layer; And based on a fifth attention mechanism anda sixth context memory, extracting semantic features of the whole comment under the entity and aspect. The position-based attention mechanism adopted by the invention can better mine the sentiment internal relations of different words and comments under different entities and aspects, thereby obtaining a more accurate prediction result.

The invention discloses an electroencephalogram signal emotion recognition method based on an attention mechanism. The electroencephalogram signal emotion recognition method comprises the steps of 1,carrying out the preprocessing of removing the baseline and segmenting the fragments on the original EEG data; 2, establishing a space-time attention neural network model; 3, training the establishedconvolutional recurrent attention network model on a public data set by adopting a ten-fold crossing method; and 4, realizing an emotion classification task by utilizing the established model. According to the invention, the high-precision emotion recognition can be realized, so that the recognition rate is improved.

The invention discloses a product review attribute-level emotion classification method based on rules and neural networks. The method includes the steps: firstly, acquiring review data and filtering Chinese participles and stop words from a review text; secondly, screening a product attribute set by the aid of a rule template, building a <attribute and review> sample set, performing emotion tagging on the attribute of each review, and building a <attribute, review and emotion> training set; building a neural network emotion classification model based on bilateral attention, and training the model by the aid of the training set; finally, filtering Chinese participles and stop words from testing data, screening a product attribute set, building a <attribute and review> testing set, and performing emotion classification by the aid of an emotion classification model. According to the method, attribute emotion category forecasting accuracy can be greatly and effectively improved by the aidof context information of attributes in the reviews.

The invention discloses a self-learning sorting method relating to multilingual data treatment, comprising the steps of extracting candidate emotion words by a first seed word Chinese or foreign language 'very', filtering stop words, and automatically obtaining a stop word list from a language database; simultaneously carrying out support or opposing clustering on the emotion words and emotion texts by a second seed word 'good' and a third seed word 'bad' or foreign languages 'good' and 'bad'; building an emotion classifier by semi-supervised learning, training the initial classifier by selecting convinced samples from a clustering result, and selecting new samples to be added into a training set by fusing emotion scores of the texts and the posterior probability of the classifier. According to the sorting method, the method facing multilingual opinion analysis is irrelevant with languages, a machine translation system and a large-scale bilingual dictionary are not needed, the emotion classifier is directly learned on a target language, the resource dependence is the smallest, and for each target language, only three seed words are needed and other priori knowledge is not needed.

The invention discloses a Chinese text sentiment analysis method based on deep learning, and belongs to the technical field of natural language processing. The defects of an unsupervised sentiment analysis method based on English are overcome. The method comprises the following steps: after converting an obtained corpus text into pinyin, pre-training a constructed language model to obtain a pre-trained language model; obtaining a small amount of text data which is in the same field as the corpus text and has emotion categories, converting the text in the text data into pinyin, and training a constructed emotion classification model based on a pre-trained language model to obtain a trained emotion analysis model; and carrying out sentiment classification on the unlabeled text by utilizing the trained sentiment analysis model to obtain a corresponding sentiment category label. The method is used for Chinese text sentiment analysis.

The invention discloses an emotion analysis method for Chinese texts based on computer information processing technology. Comments on Chinese products are subjected to word segmentation. By utilizing a bag-of-words model, vector representations of product comments are generated. The vector of every comment is inputted to a visible unit of a limited Boltzmann machine (RBM) in deep learning. Sentimental characteristics of Chinese texts are extracted by the RBM and the extracted emotional characteristics are inputted to a SVM for text emotion classification. The emotion analysis method for the Chinese texts based on computer information processing technology is capable of improving relevance of emotional semantics of characteristics while the SVM is capable of improving accuracy of emotion classification of comments on Chinese products.

The invention provides a global average pooling convolutional neural network-based Chinese emotion tendency classification method, which is a technology for analyzing a Chinese text collected from a network by utilizing a computer. The method comprises the steps of building a global average pooling convolutional neural network-based Chinese emotion tendency classification model which extracts semantic emotion features by utilizing three channel transformation convolution layers; performing pooling calculation on the features extracted by the convolution layers by a global average pooling layerto obtain confidence values corresponding to output types; and outputting emotion classification tags by Softmax. According to the method, model parameters are set for performing multi-time training,and the model with the highest classification accuracy is selected for Chinese emotion tendency classification; and, complex feature engineering in conventional emotion analysis is avoided, the semantic emotion feature extraction capability of the model is enhanced, the model over-fitting is effectively avoided, and the emotion tendency classification performance of the model is improved.

A system performs local feature extraction. The system includes a processing device that performs a Short Time Fourier Transform to obtain a spectrogram for a discrete-time speech signal sample. The spectrogram is subdivided based on natural divisions of frequency to humans. Time-frequency-energy is then quantized using information obtained from the spectrogram. And, feature vectors are determined based on the quantized time-frequency-energy information.

The invention discloses a text emotion analysis method based on a bi-directional interactive neural network, comprising the following steps: collecting entities; Text emotion corpus set, which is divided into training set and test set; Preprocessing the entities and texts in the corpus; Relative position information and adopting global word vector information to construct word and sentence representations. The entity and text word vectors of the training corpus are inputted into the neural network, training and the affective classification model. Inputting The test set entity and text word vector into the neural network model, and calculating the prediction probability of each sample. Using quantum-heuristic multi-modal decision fusion method, the weighted fusion of text prediction probability and image prediction probability is used to obtain more accurate and intelligent multi-modal emotion classification results.

The invention discloses a deep learning-based experiential word vector and emotion classification method. A context model of a word is firstly built; then, emotion information is added to the contextmodel of the word to build an experiential word vector; and finally, through an active deep confidence network method and in combination of the experiential word vector, semi-supervised emotion classification on a comment document is carried out. The problem that the existing word vector learning algorithm generally only uses the context of the word but ignores the emotion information of the textis solved.

The present invention discloses an overall emotion recognition method and system combining an image and speech. The process of recognition comprises: after acquiring a corresponding speech and video signal from an input video, an information acquisition apparatus transmits the corresponding speech and video signal to corresponding emotion classification modules respectively, and after classification, an integrated learning trainer allocates weights, and after weighting, a recognition result is output to complete a recognition process. The system comprises an information acquisition apparatus, an emotion classifier and an integrated processor. The information acquisition apparatus comprises a video acquisition device and an audio acquisition device; the emotion classifier comprises an expression emotion classification module for performing emotion classification on acquired video information and a speech emotion classification module for performing emotion classification on acquired audio information; and the integrated processor comprises a weighting module and an integrated learning trainer. The method and system provided by the present invention have the advantages of higher emotion classification reliability, flexible adjustment on confidence parameters and high precision; and through bi-directional recognition of expression and speech, the human emotion recognition process is simulated to a large extent.

The invention relates to an emotion classification method and an emotion classification system. The classification method comprises the following steps: preprocessing the data of a sample to be tested; getting a feature word set of the sample to be tested; using a naive Bayes algorithm to perform calculation on the feature word set of the sample to be tested to generate the probability that the feature word set of the sample to be tested belongs to a category; and using a support vector machine to correct the probability that the feature word set of the sample to be tested belongs to a category, and determining the classification of the sample to be tested. According to the invention, Internet user emotion under the perspective of cross-cultural communication is analyzed in a more fine-grained way by building an emotional dictionary and emotion feature words in the field of cross-cultural communication, and the accuracy of emotion classification is improved.

The invention discloses a Chinese song emotion classification method based on multi-modal fusion. The Chinese song emotion classification method comprises the steps: firstly obtaining a spectrogram from an audio signal, extracting audio low-level features, and then carrying out the audio feature learning based on an LLD-CRNN model, thereby obtaining the audio features of a Chinese song; for lyricsand comment information, firstly constructing a music emotion dictionary, then constructing emotion vectors based on emotion intensity and part-of-speech on the basis of the dictionary, so that textfeatures of Chinese songs are obtained; and finally, performing multi-modal fusion by using a decision fusion method and a feature fusion method to obtain emotion categories of the Chinese songs. TheChinese song emotion classification method is based on an LLD-CRNN music emotion classification model, and the model uses a spectrogram and audio low-level features as an input sequence. The LLD is concentrated in a time domain or a frequency domain, and for the audio signal with associated change of time and frequency characteristics, the spectrogram is a two-dimensional representation of the audio signal in frequency, and loss of information amount is less, so that information complementation of the LLD and the spectrogram can be realized.

The invention discloses a multi-hop attention and depth model, method, storage medium and terminal for classification of target sentiments. In said model, the combined two-dimensional lexical features (matrix3) produced by the first convolution operation module are used in each hop of attention calculation module and the attention weight information is continuously transmitted to sublayers; before calculation in the last hop, the one-dimensional lexical features input are weighted (by lexical vector weighting module) in the model with the attention (the first attention calculation module) before convolution operation (the second convolution operation module), to generate the weighted combined two-dimensional lexical features (matrix4) to be used in the final attention calculation.

The invention provides a comment text aspect-level sentiment classification method based on deep learning. The method comprises the following steps: preprocessing a comment text, including word segmentation and stop word removal, balancing aspect words and corresponding tags to generate a balanced sample, and vectorizing the balanced sample and Chinese words in an original sample to obtain word vectors in the balanced sample; inputting the word vectors into the model to predict a comment result, wherein the model is a deep learning model constructed according to a deep neural network, the similarity calculation is carried out on word vectors of aspect words and other words of sentences, and an aspect emotion semantic matrix of a balance sample is generated. According to the method, throughthe balance processing and construction of the Attn-Bi-LCNN model, the emotion semantic matrix can be effectively output, and the accuracy of the model and the prediction speed in practical application are improved, so the method is suitable for aspect-level fine-grained emotion classification of texts.

The invention discloses a text sentiment classification method and system, and the method comprises the steps: dividing a text sentence in terms of words, and mapping each word into a word vector; extracting keywords in the text sentences, respectively constructing a word vector attention matrix, a position attention matrix and a part-of-speech attention matrix according to word vectors of the keywords, positions of the keywords in the text sentences and emotion part-of-speech types to which the keywords belong, and fusing the word vector attention matrix, the position attention matrix and thepart-of-speech attention matrix to construct a first feature; adopting a BiGRU network to obtain a second feature according to the context semantic information of the keyword; and classifying the emotion types of the text sentences to be tested by adopting a multi-attention convolutional neural network model trained by taking the first features and the second features as a training set. Accordingto the method, a keyword sentiment classification first-dimensional feature is obtained in combination with a CNN model of a multi-attention mechanism, an initial sentence sentiment classification second-dimensional feature is obtained through BiGRU, the two dimensional features are fused, the text deep-level semantic perception capability is improved, and then the text sentiment classification accuracy is improved.

The invention provides a text abstract and sentiment classification combined training method, which is realized by adopting a text abstract and sentiment classification combined model, and specifically comprises the following steps of: preprocessing a text, and constructing a training set vocabulary; constructing a text abstract model, and carrying out text abstract task pre-training; adding an emotion classification layer on the basis of a text abstract model, constructing a layered end-to-end model, and jointly training emotion classification and text abstract tasks. The invention provides atext abstract and sentiment classification combined training method. Through combined training of the two types of tasks, the content consistency between the generated abstract and the input text canbe improved, the generated abstract can better contain emotion information of the input text, key information of the input text is extracted through the abstract task, and emotion prediction is moreaccurate.