Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics |
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| Institution: | 1. School of Information, Central University of Finance and Economics, Beijing 100081, China;2. Business School, Beijing Normal University, Beijing 100875, China;1. University of Sussex Business School, University of Sussex, United Kingdom;2. School of Management, Royal Holloway, University of London, United Kingdom;3. Kent Business School, University of Kent, United Kingdom;1. School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China;2. Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Jinan 250358, China;3. Shandong State Key Laboratory of High-end Server & Storage Technology, Jinan 250101, China;1. School of Business, Taishan University, Taian, 271000, China;2. Post-Doctoral Scientific Research Workstation, China Merchants Bank, Shenzhen, 518040, China;3. College of Business and Administration, Zhejiang University of Technology, Hangzhou, 310014, China;4. School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China;1. Department of Engineering Science and Mathematics, Luleå University of Technology, Sweden;2. Hydcon KB, Sweden |
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| Abstract: | The breeding and spreading of negative emotion in public emergencies posed severe challenges to social governance. The traditional government information release strategies ignored the negative emotion evolution mechanism. Focusing on the information release policies from the perspectives of the government during public emergency events, by using cognitive big data analytics, our research applies deep learning method into news framing framework construction process, and tries to explore the influencing mechanism of government information release strategy on contagion-evolution of negative emotion. In particular, this paper first uses Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public emergencies-oriented emotional lexicon; then, it proposes a emotion computing method based on dependency parsing, designs an emotion binary tree and dependency-based emotion calculation rules; and at last, through an experiment, it shows that the emotional lexicon proposed in this paper has a wider coverage and higher accuracy than the existing ones, and it also performs a emotion evolution analysis on an actual public event based on the emotional lexicon, using the emotion computing method proposed. And the empirical results show that the algorithm is feasible and effective. The experimental results showed that this model could effectively conduct fine-grained emotion computing, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that due to such defects as slow speed, non transparent content, poor penitence and weak department coordination, the existing government information release strategies had a significant negative impact on the contagion-evolution of anxiety and disgust emotion, could not regulate negative emotions effectively. These research results will provide theoretical implications and technical supports for the social governance. And it could also help to establish negative emotion management mode, and construct a new pattern of the public opinion guidance. |
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| Keywords: | Government information release Cognitive big data analytics E-government Sentiment analysis Public emergency events |
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