Cross-domain emotion classification system and method based on hierarchical attention mechanism

Abstract

The invention relates to a cross-domain emotion classification system based on a hierarchical attention mechanism, and the system comprises a text preprocessing module which is used for the characterization of a cross-domain text; a pivot feature extraction module, used for learning a feature representation space adapted to the field to obtain pivot feature document representation of the source field and the target field; a non-pivot feature extraction module, used for acquiring non-pivot feature representation; and an emotion category output module, used for obtaining a final emotion classification result. According to the method, efficient cross-domain emotion classification is realized, the cross-domain emotion classification precision is improved, and the consumption of manual time andenergy is reduced.

Description

technical field [0001] The present invention relates to the fields of sentiment analysis and opinion mining, and in particular to a cross-domain sentiment classification system and method based on a layered attention mechanism. Background technique [0002] Cross-domain sentiment classification is to leverage the knowledge of related source domains and rich labeled data to improve the target domain. However, user emotion expression has different performances in different domains. For example, in the field of books, words such as readable and thoughtful are used to express positive emotions, while words such as bland and plotless are often used to express negative emotions. Due to the differences in domains, sentiment classifiers trained in the source domain may not work well if they are directly applied to the target domain. To address this problem, researchers have proposed various methods for cross-domain sentiment classification. [0003] At present, research on cross-...

AI Technical Summary

Problems solved by technology

These methods lack interpretability and cannot fully prove whether the network has fully learned the pivot features, and there is still a lot of room for exploration

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