An aspect-level emotion classification model and method based on dual-memory attention

Abstract

The invention discloses an aspect-level emotion classification model and method based on dual-memory attention, belonging to the technical field of text emotion classification. The model of the invention mainly comprises three modules: an encoder composed of a standard GRU loop neural network, a GRU loop neural network decoder introducing a feedforward neural network attention layer and a Softmaxclassifier. The model treats input statements as a sequence, based on the attention paid to the position of the aspect-level words in the sentence, Two memory modules are constructed from the originaltext sequence and the hidden layer state of the encoder respectively. The randomly initialized attention distribution is fine-tuned through the attention layer of the feedforward neural network to capture the important emotional features in the sentences, and the encoder-decoder classification model is established based on the learning ability of the GRU loop neural network to the sequence to achieve aspect-level affective classification capabilities. The invention can remarkably improve the robustness of the text emotion classification and improve the classification accuracy.

Description

technical field [0001] The invention belongs to the technical field of text emotion classification, in particular to the technical field of natural language processing, and specifically relates to an aspect-level emotion classification model and method based on a dual-memory attention mechanism and an encoder-decoder structure. Background technique [0002] Sentiment analysis, also known as opinion mining, is a research field that analyzes people's subjective feelings such as opinions, emotions, evaluations, opinions and attitudes towards physical objects such as products, services, organizations, individuals, events, topics and their attributes. Aspect-level sentiment analysis is to analyze the emotional tendency (positive, negative or Neutral), which is a subdivision task of sentiment analysis and one of the fundamental concerns in this field. [0003] Traditional feature representation methods include One-hot, N-Gram, and some effective features designed by domain expert...

AI Technical Summary

Problems solved by technology

[0004] Among the existing solutions, the cyclic neural network model based on the attention mechanism and the multi-layer model based on the attention mechanism perform better. The reason for the better performance of the former is that with the help of the feature abstracti...

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