Specific target emotion classification method based on attention coding and graph convolution network

Abstract

The invention provides a specific target emotion classification method based on attention coding and a graph convolution network, and the method comprises the steps: obtaining a context and a hidden state vector corresponding to a specific target through a preset bidirectional recurrent neural network model, and carrying out the multi-head self-attention coding of the context and the hidden statevector; extracting a syntax vector in a syntax dependency tree corresponding to the context by combining a point-by-point convolution graph convolutional neural network, and performing multi-head self-attention coding on the syntax vector; then, multi-head interaction attention is used for carrying out interaction fusion on syntactic information codes, context semantic information codes, syntacticinformation codes and specific target semantic information codes; and splicing the fused result with the context semantic information code to obtain a final feature representation, and obtaining an emotion classification result of the specific target based on the feature representation. Compared with the prior art, the relation between the context and the syntax information and the relation between the specific target 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 a specific target emotion classification method based on attention coding and graph convolutional 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, target-specific sentiment analysis belongs to fine-grained sentiment analysis, which is different from traditional sentiment analysis, and its purpose is mainly to identify the emotional polarity of a specific target in a sentence. [0003] At present, there are many methods combining neural network and attention mechanism to solve the problem of emotional analysis of specific targets. Although these methods can overcome the defects of shallow learning models and distinguish the importance of different words for specific target emotional analysis tasks, However, there a...

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Problems solved by technology

[0003] At present, there are many methods combining neural network and attention mechanism to solve the problem of emotional analysis of specific targets. Although these methods can overcome the defects of shallow learning models and distinguish the importance of different words for specific target emotional analysis tasks, However, there are still the following problems: on the one hand, the existing methods cannot fully capture the semantic information of the context, and do not pay attention to the problems of long-distance information dependence and information parallel computing; on the other hand, the existing methods do not consider the context and specific goals and syntactic information , and due to the lack of syntactic constraints, syntactically irrelevant context words may be recognized as clues to judge the target sentiment classification, reducing the accuracy of classification

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