A specific target emotion analysis method based on a multi-channel model

Abstract

The invention discloses a specific target emotion analysis method based on a multi-channel model. According to the specific target emotion analysis method, target words and contexts are fully utilized, the method is provided with three channels, and expression of the target words and the contexts is obtained through a hierarchical pooling mechanism, an interaction attention mechanism and an attention mechanism based on the Euclidean distance. Through the three channels, the target words and the context can learn the expression helpful for sentiment classification; the technical scheme is as follows: (1) inputting a SemEval2014 data set, preprocessing the data set and dividing the data set into a training set and a test set; (2) inputting the preprocessed data into three channels respectively, and performing feature extraction to obtain vectors r1, r2, r3, r4 and r5; (3) obtaining a classification result through learning of an attention mechanism by utilizing vectors r1, r2, r3, r4 andr5; (4) carrying out sentiment classification on a specific target of each comment text in the test set by using the trained model, and comparing the sentiment classification with a label of the testset to calculate the classification accuracy; the invention belongs to the fields of natural language processing technology and sentiment calculation.

Description

technical field [0001] The invention belongs to the field of natural language processing technology and emotional computing, in particular to an English specific target emotional analysis method based on a multi-channel deep learning model. Background technique [0002] With the development of the e-commerce industry, online shopping is more and more recognized by people, which also produces a large amount of online comment text data. Faced with these massive online reviews, on the one hand, consumers need to quickly understand the emotional tendencies of the reviews, get evaluation information about the item from other consumers’ experience, and optimize their purchase decisions; on the other hand, businesses also need to learn from consumers Summarize the market feedback information of the product from the emotional tendency of online comments, and improve the product. Therefore, how to sentimentally classify review texts has become an important research topic in the fiel...

AI Technical Summary

Problems solved by technology

Although the above methods have achieved certain effects, the information obtained by these methods is not comprehensive enough, resulting in unreliable analysis results.

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