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

A convolutional neural network and user comment technology, applied in semantic analysis, special data processing applications, text database clustering/classification, etc., can solve the problems of reducing the accuracy of sentiment analysis and not taking into account the different contributions The effect of time and labor costs, overcoming defects, and fast convergence speed

Pending Publication Date: 2019-01-29
CHONGQING UNIV OF POSTS & TELECOMM
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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 co

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

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Embodiment Construction

[0022] The specific implementation method of the present invention will be further explained below in conjunction with the accompanying drawings. The present invention mainly adopts a modular design, and mainly consists of four parts, including: a word embedding layer, a convolutional layer, an attention module and a classifier. figure 1 It is a system structure diagram of the present invention. Among them, the word embedding layer is used to vectorize the input comment text, that is, to convert the text into a vector form. The word embedding method represents each word with a low-dimensional vector, and concatenates the vector representations of all words in each comment to form a vector representation of a comment. The convolutional layer learns local features in the input through the spatial structure relationship to reduce the number of parameters that the model needs to learn. The present invention uses a convolutional layer to extract local features of comments, which ...

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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...

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Application Information

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IPC IPC(8): G06F17/27G06F16/35
CPCG06F40/284G06F40/30
Inventor 徐光侠郑爽刘俊周由胜程金伟赵娟袁野
Owner CHONGQING UNIV OF POSTS & TELECOMM
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