Dependency tree and attention mechanism-based attribute sentiment classification method

Abstract

The invention discloses a dependency tree and attention mechanism-based attribute sentiment classification method. The method comprises the steps of selecting a smallest sub-tree part comprising givenattributes based on a dependency tree analysis result of a whole text, and taking clauses of the part as representations of context information of the attributes; then performing modeling on contextsof sentences and contexts of the attributes by utilizing two bidirectional threshold circulation units to obtain two feature representation matrixes fixed in size; obtaining feature representations of the text and specific attributes by utilizing an attention mechanism; and finally performing sentiment polarity classification of the specific attributes by utilizing a multilayer perceptron. The classification method proposed by the invention can extract different attribute feature information for different attributes in the same text, and is high in classification accuracy.

Description

technical field [0001] The invention relates to the fields of computer application technology, natural language processing, emotion analysis technology, etc., and in particular to an attribute emotion classification method based on a dependency tree and an attention mechanism. Background technique [0002] Sentiment analysis, also known as opinion mining, is a hot field in natural language processing, which aims to analyze people's emotions and opinions on certain things from text in an automated way. With the rapid development of the Internet, the network has become the main way for people to communicate and obtain information. The emotional tendency and opinions expressed by things provide basic support for the next specific application, such as market decision-making. [0003] Text sentiment analysis technology has shown great appeal to both academia and industry. On the one hand, sentiment analysis involves a number of very challenging tasks, such as identifying the ho...

AI Technical Summary

Problems solved by technology

[0009] The current lexicon-based attribute sentiment classification method relies heavily on the sentiment lexicon. The quality of the lexicon determines the correctness of the classification results. At the same time, the lexicon-based and rule-based methods also show strong domain dependence. Different rules need to be designed, which is difficult for practical application

The method based on statistical learning also has the problem of manually designing specific features for data in different fields, which requires a lot of manual time for feature extraction, and requires high domain knowledge.

The method based on deep learning can automatically extract features from the text, but these features lack the correlation with attributes, and the classifier cannot accurately analyze the different emotional tendencies of different attributes in the same text

Images