Text attribute word sentiment classification method based on deep learning network

Abstract

The invention provides a text attribute word sentiment classification method based on a deep learning network. The text attribute word sentiment classification method comprises the steps of obtaininga word vector corresponding to a target sentence in a text; inputting the word vector into a hidden information extraction network model to obtain a hidden state vector; extracting first syntax information in a syntax dependency tree corresponding to the target sentence based on the hidden state vector and the first syntax extraction neural network; based on the hidden state vector and a second syntax extraction neural network, extracting second syntax information in a local syntax dependency tree corresponding to the target sentence; denoising the first syntax information and the second syntax information to obtain context representation and attribute word representation; averaging and pooling the context representation and the attribute word representation, and then splicing to obtain feature representation corresponding to the target sentence; and inputting the feature representation into an emotion classification function to obtain an emotion classification result. Compared with the prior art, the relation between the target sentence and the syntax information and the relation between the attribute words and the syntax information are fully considered, and the accuracy of sentiment classification is improved.

Description

technical field [0001] The invention relates to the technical field of natural language processing, in particular to an emotion classification method for text attribute words based on a deep learning network. Background technique [0002] Sentiment analysis is an important task in Natural Language Processing (NLP), and its purpose is to analyze subjective text with emotional color. Among them, the sentiment analysis of text attribute words is different from traditional sentiment analysis, and its purpose is mainly to identify the emotional polarity of attribute words in sentences in the text. [0003] At present, there are many neural network methods used to solve the problem of sentiment analysis of attribute words. Although these methods can overcome the defects of shallow learning models and distinguish the importance of different words for the task of attribute word sentiment analysis, there are still the following problems: 1. On the one hand, the existing method canno...

AI Technical Summary

Problems solved by technology

[0004] The embodiment of the present application provides a method for emotional classification of text attribute words based on a deep learning network, which can solve the problem of low accuracy of text attribute word emotion classification results. The technical solution is as follows:

Images