Open Access

ARTICLE

An Emotion Analysis Method Using Multi-Channel Convolution Neural Network in Social Networks

Xinxin Lu^1,*, Hong Zhang²

1 School of Computer Science and Technology, Zhoukou Normal University, Zhoukou, 466001, China
2 School of Network Engineering, Zhoukou Normal University, Zhoukou, 466001, China

* Corresponding Author: Xinxin Lu. Email:

(This article belongs to the Special Issue: Recent Advances on Deep Learning for Medical Signal Analysis (RADLMSA))

Computer Modeling in Engineering & Sciences 2020, 125(1), 281-297. https://doi.org/10.32604/cmes.2020.010948

Received 09 April 2020; Accepted 05 June 2020; Issue published 18 September 2020

Abstract

As an interdisciplinary comprehensive subject involving multidisciplinary knowledge, emotional analysis has become a hot topic in psychology, health medicine and computer science. It has a high comprehensive and practical application value. Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research. The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period, so as to understand their normal state, abnormal state and the reason of state change from the information they wrote. In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences, and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model, an emotional analysis model using the emotional dictionary and multichannel convolutional neural network is proposed in this paper. Firstly, the input matrix of emotion dictionary is constructed according to the emotion information, and the different feature information of sentences is combined to form different network input channels, so that the model can learn the emotion information of input sentences from various feature representations in the training process. Then, the loss function is reconstructed to realize the semi supervised learning of the network. Finally, experiments are carried on COAE 2014 and self-built data sets. The proposed model can not only extract more semantic information in emotional text, but also learn the hidden emotional information in emotional text. The experimental results show that the proposed emotion analysis model can achieve a better classification performance. Compared with the best benchmark model gram-CNN, the F1 value can be increased by 0.026 in the self-built data set, and it can be increased by 0.032 in the COAE 2014 data set.

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