Text sentiment analysis method based on graph attention network

Abstract

The invention provides a text sentiment analysis method based on a graph attention network. The text sentiment analysis method comprises the following steps: 1, obtaining a text set and an emotion label set from a Semeval 2014 Task 4 data set; step 2, performing random selection in the text set and the emotion label set in proportion to obtain a training set and a test set; 3, performing syntacticdependency relationship analysis on the sentences in the training set through a Biaffine dependency parser, and constructing a syntactic dependency graph according to the syntactic dependency relationship of the sentences; and step 4, inputting the training set into a BERT pre-training model, and converting words in the training set into word vectors through the BERT pre-training model. Accordingto the invention, the syntactic dependency relationship between sentences is analyzed through the Biaffine dependency analyzer, word vector representation is obtained through the BERT pre-training model, sentiment analysis is conducted on the text through the graph attention network model, the complex syntactic structure in the text is fully utilized, and the text sentiment analysis accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of natural language processing, in particular to a text sentiment analysis method based on a graph attention network. Background technique [0002] In recent years, with the rapid development of the Internet, people are accustomed to expressing their emotions or opinions on social networks or e-commerce websites, resulting in a large number of online reviews on the Internet. One online review may include multiple evaluations of the same entity. There are further requirements for text sentiment analysis. Aspect-level sentiment analysis (ABSA) is a fine-grained task in text sentiment analysis. Tasks provide important emotional information and are also one of the research hotspots in the field of natural language processing. [0003] Researchers have done a lot of research on aspect-level text sentiment analysis. Most of the early research on text sentiment analysis used to extract and learn text features to b...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a text sentiment analysis method based on a graph attention network, and its purpose is to solve the problem that traditional sentiment analysis methods are difficult to capture the syntactic dependencies between aspects in sentences and to express complex syntactic structures in texts, and the text The problem of low accuracy of sentiment classification

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