A user comment emotion analysis system and method based on attention convolution neural network

Abstract

The invention discloses a user comment emotion analysis system and a method based on an attention convolution neural network. The invention is a modular design and mainly comprises four modules, namely, a word embedding module, a convolution module, an attention module and a classifier module. The word embedding module uses the low-dimensional vector to represent the comment text, the convolutionmodule extracts the local features of the comment by convolution operation, the attention module determines the weight of the local features by comparing the similarity, and calculates the final feature expression of the comment by weighting, and the classifier module classifies the emotion according to the final feature expression. The invention overcomes the shortcomings of the traditional neural network model feature extraction method by adding the attention mechanism into the neural network model. After a large number of data training, the attention mechanism can judge the importance of different words in comments, so that the model can notice the part of comments that has the greatest impact on emotion, and improve the accuracy of emotion classification.

Description

technical field [0001] The invention relates to a sentiment analysis method for Internet user comments, which mainly combines word embedding technology and convolutional neural network to learn the features of user comments, and combines attention mechanism to improve the accuracy of sentiment classification, which belongs to natural language processing and artificial intelligence cross field. Background technique [0002] In recent years, more and more users are used to posting their views and comments on a certain thing on the Internet. How to quickly and accurately analyze the user emotions contained in the massive comment information on the Internet has become a research hotspot in the field of information science and technology. The most basic task in user comment sentiment analysis is to classify users' emotional tendencies, including binary sentiment classification and multivariate sentiment classification. Since a large amount of comment data can be obtained on the...

AI Technical Summary

Problems solved by technology

These two feature extraction methods do not take into account the different contributions of different parts of user comments to the final sentiment of the comment. Although the effect is better for a single sentence or phrase, it will reduce the accuracy of sentiment analysis when facing longer user comments. sex

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