文献类型：期刊文献

英文题名：Negative Emotion Recognition Algorithm of Network Catchwords Based on Language Feature Dimension

作者：Wang, Min[1]; Chen, Tian[2]; Xiao, Yanjun[3,4]

机构：[1] College of Foreign Languages, Shaoxing University, Zhejiang, China; [2] Foreign Studies College of Hunan Normal University, Hunan, Changsha, China; [3] University of Science, Malaysia; [4] Hunan Institute of Technology, Hunan, China

年份：2022

卷号：30

期号：4

起止页码：275

外文期刊名：Journal of Computing and Information Technology

收录：EI(收录号：20234915161893)、Scopus(收录号：2-s2.0-85178431252)

语种：英文

外文关键词：Emotion Recognition - Gaussian distribution - Hidden Markov models - Neural network models - Speech recognition - Trellis codes

外文摘要：The traditional negative emotion recognition algorithm has a limited language feature dimension, which leads to the inaccuracy of negative emotion recognition. In order to improve the identification and analysis of emotion in network buzzwords, the back propagation of error (BP) and the restricted Boltzmann machine (RBM) algorithms are adopted to effectively solve the problem of insufficient data for emotion analysis in different contexts. First, a method is proposed to identify negative emotions, and a deep neural network (DNN) model is constructed. Then, experiments were carried out, which used manually labeled data sets and divided them into different emotion categories, and which compared the BP algorithm, Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) for negative emotion recognition of online buzzwords. The experimental results show that the DNN model performs well in the recognition of anger, sadness, fear and disgust, with the accuracy reaching 93.56%, 93.58%, 89.84% and 88.53% respectively, which is obviously superior to the other three methods. The designed DNN model has a potential application pros-pect in the negative emotion recognition of online buzzwords, which can be further popularized in the future. ? 2022, University of Zagreb Faculty of Electrical Engineering and Computing. All rights reserved.