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| Negative Emotions of Online Users’ Analysis Based on Bidirectional Long Short-Term Memory |
| Wu Peng1,2, Ying Yang2, Shen Si1,2 |
1. School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094;
2. Jiangsu Collaborative Innovation Center of Social Safety Science and Technology, Nanjing 210094 |
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Abstract The negative emotions of netizens are of great significance in the analysis of network public opinions; however, existing research lacks a multiclassification method to identify automatically the negative emotions of online users in massive short text. This study uses word embedding to study the features of the word sequence, learning the sentiment-encoded word vector by increasing the emotional features of the context. The bidirectional long short-term memory (LSTM) model is trained to obtain the online users’ negative-emotion recognition model, and the online users’ anger, sadness, and fear are identified on the basis of judging the online users’ sentiment polarities. Then, the case data are compared with experimental results of models such as support vector machine (SVM), LSTM, and convolutional neural network (CNN). The experimental results show that sentiment-encoded word embedding is more suitable than word embedding for sentiment analysis. The bidirectional long short-term memory model has good sentiment analysis performance. Classifying the negative emotions of the netizen based on the identification of sentiment polarities is better than directly distinguishing negative emotions.
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Received: 26 March 2018
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