Attribute-level sentiment analysis method based on hierarchical attention mechanism and gate mechanism

Abstract

The invention discloses an attribute-level sentiment analysis method based on a hierarchical attention mechanism and a gate mechanism, and aims to make full use of the relationship between contexts and attribute words to make the contexts and the attribute words fully associated. Highlighting the importance of different words in the context and the attribute words so as to improve the attribute-level sentiment analysis precision; the method enriches information in context and attribute word representation, and adopts the scheme that comment corpora are preprocessed, and a context word embedding matrix and an attribute word embedding matrix are obtained through GloVe word vector indexing; inputting the context and the attribute words into the GRU to obtain a context hiding state and an attribute word hiding state; obtaining a context vector representation 1, an attribute word vector representation and a context vector representation 2 through self-attention; splicing to obtain an overall vector representation, obtaining distribution of sentiment polarities, corresponding to contexts, of attribute words through a classifier, analyzing differences, and adjusting parameters in the model; the method belongs to the field of sentiment analysis in natural semantic processing.

Description

technical field [0001] The invention relates to an emotion analysis method, in particular, an attribute-level emotion analysis method based on a hierarchical attention mechanism and a gate mechanism; it belongs to the field of emotion analysis in natural semantic processing. Background technique [0002] In recent years, with the development of the Internet, a large number of consumer platforms and social networking platforms have gradually entered people's lives. Consumers often leave a comment on the pros and cons of a product after consumption on a consumer platform, and netizens also leave their opinions on a certain event. For merchants, these comments containing emotional information are extremely valuable for both enterprises and governments. Enterprises can understand the shortcomings of products by analyzing consumer comments, so as to achieve the purpose of improving product performance. Government It can also guide the development direction of the event by summar...

AI Technical Summary

Problems solved by technology

However, simple splicing cannot achieve the effect of identifying important words for attribute words in the context

Second, in most attribute-level sentiment analysis work, the results of the attention mechanism between context and attribute words are directly used as the expression of context and attribute words. This paper argues that this approach ignores the role of the original context and attribute word representation to a certain extent

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