Short text sentiment classification method based on CNN-BiMGU model

Abstract

The invention discloses a short text sentiment classification method based on a CNN-BiMGU model, and belongs to the technical field of deep learning and natural language processing. The CNN-BiMGU model mainly comprises an embedding layer, a convolution layer, a pooling layer, a BiMGU layer, an attention mechanism layer, a full connection layer and a classification layer, wherein the embedding layer encodes a data set containing commodity comments into word vectors, the convolution layer extracts text feature matrixes from the word vectors through a CNN channel, the pooling layer performs dimension reduction on the text feature matrixes, and the BiMGU layer is used for obtaining hidden state vectors; wherein the attention mechanism layer is used for strengthening important information, thefull connection layer is used for splicing output of the pooling layer and the attention mechanism layer, and the classification layer is used for obtaining final emotion categories in a classified mode. According to the CNN-BiMGU model, the CNN channel and the BiMGU channel are connected in parallel, the defect that the CNN channel ignores the relation before and after the features is overcome, the attention mechanism is fused into the BiMGU channel, important features in context semantic features are highlighted, and the accuracy of short text emotion classification is improved.

Description

technical field [0001] The invention relates to a short text emotion classification method based on a CNN-BiMGU model, and belongs to the technical fields of deep learning and natural language processing. Background technique [0002] Text sentiment analysis refers to the process of analyzing, processing and extracting subjective text with emotional color by using natural language processing and text mining technology. At present, text sentiment classification research involves many fields, including natural language processing, machine learning, information extraction and information retrieval, etc., and has attracted the attention of many researchers. Online reviews on shopping platforms, for merchants, can help them understand the advantages and disadvantages of products in a timely manner and make corresponding improvements, so as to provide consumers with better services, further increase the sales of stores and obtain better profits; For users, the emotional tendency ...

AI Technical Summary

Problems solved by technology

Long short-term memory network (LSTM) can effectively extract the semantic information of long-distance context, but the structure is complex and requires a lot of time and memory space. Although it achieves a good classification effect, the training efficiency is low

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