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 abstraction mechanism of the deep learning model, more accurate attention distribution, and the latter uses the attention captured by the previous layer to help the next layer to calculate a more accurate attention distribution. Very important word-level or phrase-level emotional features

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