Research On Construction Method Of Dynamic Emotional Dictionary Based On Bidirectional LSTM And Text Emotional Analysis

With the rapid development of social media,weibo occupies a large proportion of social media.Most weibos express netizens' emotional tendencies towards an event,a person or a product.Using emotional analysis technology to help analyze the emotions of the text can better quickly sort out and analyze these information,so as to obtain the tendency of public opinion.In order to study text affective analysis better,it is a key link to construct a high-quality affective dictionary.A high-quality affective dictionary can effectively help improve the quality of affective analysis.This paper proposes a method to construct a dynamic emotional dictionary.On the basis of the emotional dictionary,emotional analysis is carried out.The emotional score of the text is obtained by using cyclic neural network and attention mechanism.The polarity and polarity of the text are judged according to the emotional score of the text.The main research contents include:(1)Constructing a dynamic emotional dictionary.Firstly,by improving the CBOW model to ECBOW model,emotional features are obtained by adding part of network structure to the original CBOW model.Then,the semantic dependency relationship is described by a Huffman semantic binary tree,and the binary semantic dependency path information of each word is trained by using the bidirectional LSTM neural network to obtain the binary semantic path features.Finally,the distance between words and the central word is added.Features and information features of the central words are input into the biLSTM neural network as the overall features of the vocabulary,and supervised training of the biLSTM neural network is carried out to obtain the dynamic affective dictionary.(2)Text emotional classification.Using biGRU as the basis of deep network analysis,this paper proposes a text sentiment scoring method based on semantic attention mechanism.Firstly,according to the structure of semantic dependency tree,the sequence of semantic dependency paths from text words to emotional words is obtained,and then the sequence of semantic dependency paths is input into thefeature network to extract the feature of semantic dependency paths.Then,according to the attention mechanism,the influence of emotional words on other words in the text is observed to distribute the attention coefficient,and then the weight of emotional score is affected.Then,the attention mechanism is combined.The final text emotional score is calculated by the text emotional score method.Finally,the polarity and polarity of the text emotional score are judged according to the text emotional score.Based on emotional analysis,this paper classifies text emotions by semantic attention mechanism.The experimental results show that the quality of the emotional dictionary constructed in this paper is higher than other emotional dictionaries,and its classification accuracy is improved by 2.98%.Moreover,the text emotional scoring method based on semantic attention mechanism is relatively based on traditional SVM text emotional classification method.The accuracy of classification was improved by 11.17%.