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| Research on the Collaborative Model of Sentiment Analysis and Topic Mining of Micro-blogging Users in the Context of COVID-19 |
| Wang Xiwei1,2,3,4, Li Yueqi1, Liu Tingyan1, Zhang Liu1 |
1.School of Management, Jilin University, Changchun 130022
2.Research Center for Big Data Management, Jilin University, Changchun 130022
3.Research Center of Cyberspace Governance, Jilin University, Changchun 130022
4.Academy of Northeast Revitalization, Jilin University, Changchun 130022 |
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Abstract In the context of COVID-19, understanding users' emotions and the topics underlying them can help the relevant public departments improve their network governance capabilities. Based on the cognitive emotion evaluation model, this study constructed a collaborative model of sentiment analysis and topic mining using the Naive Bayesian Model to classify the sentiments. Further, it extracted the topics based on the improved Latent Dirichlet Allocation (LDA) topic model of the relevance formula. This study selected the event “Japan Diamond Cruise” amid COVID-19 as an example to draw a collaborative analysis including sentiment analysis and topic mining. Additionally, it studied the timeline of these events and determined the types of the topics through empirical tests to verify their emotion characteristics and the topics that users pay attention to. The results showed that the model proposed in this paper can reveal the topic and emotion characteristics at each stage of the event. The method of collaborative analysis of sentiment and topic presented in this paper can provide some guidance for public opinion supervision departments in improving the efficiency of public opinion guidance and achieving network ecological governance.
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Received: 30 April 2020
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