Text emotion reason identification method based on D-LSTM

Abstract

The invention belongs to the field of natural language processing text emotion analysis, and relates to a method for identifying a text emotion reason identification method. The method mainly comprises the following steps: obtaining a text containing candidate emotion reasons and emotion descriptions; converting the clauses into a word embedding matrix; usingbi-directional long short term memory network Bi-LSTM to encode the context information of the clauses; learning semantic relationships between the emotion description clauses and the candidate cause clauses by using an attention mechanism; for the emotion description clause set, extracting a local maximum semantic meaning by using a convolutional neural network CNN; using the Bi-LSTM to encode context information between the clauses;splicing the emotion description clause set and the coded candidate reason clause, and judging whether the emotion description clause set and the coded candidate reason clause have an emotion initiation relationship or not by using a multi-layer perceptron network MLP. According to the method, the problem that semantic relations between the emotion reason clauses and the emotion description clauses and between the emotion reason clauses are not fully considered in a traditional method is solved. Therefore, the invention provides a method for fusing the context of the clause and the context ofthe sentence, so that the emotion reason identification accuracy is improved.

Description

technical field [0001] The invention belongs to the field of natural language processing text emotion analysis, and in particular relates to a method for identifying emotional causes of text. Background technique [0002] In recent years, the rapid development of the Internet has made information dissemination no longer restricted by time and space, and the rise and rapid development of social media have allowed users to express their views and exchange opinions anytime and anywhere. Forums, blogs, WeChat, Weibo, Twitter, even shopping reviews, emails... People communicate with each other and express their opinions all the time in cyberspace. Text has become the most commonly used expression in cyberspace. These texts with the publisher's emotional information have always attracted the attention of researchers. Acquiring and understanding the emotional information of these texts is becoming more and more important in some applications and decision-making, so text sentiment...

AI Technical Summary

Problems solved by technology

For example, the rule-based method aims at constructing rules for the corpus and cannot be generalized; while the statistical-based method needs to extract the feature vector space for the corpus and relies on a large-scale corpus

The recognition method based on rules and statistics does not fully consider the semantic connection between the emotion cause clause and the emotion description clause, and between the emotion cause clauses

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