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| Emotion Prediction of Network Public Opinions Based on the Deviation Rules Markov Model |
| Shi Wei1,2, Xue Guangcong2, He Shaoyi3 |
1.School of Economics and Management, Zhejiang Ocean University, Zhoushan 316022
2.School of Information Engineering, Huzhou University, Huzhou 313000
3.College of Business and Public Administration, California State University, San Bernardino, San Bernardino 91708 |
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Abstract Using sentiment analysis technology, the evolution of online public opinion can be predicted from the perspective of emotion transfer in short microblog comments. In this study, the short texts of related microblog comments in the early stage of the COVID-19 pandemic were taken as the research object. Based on the extended association rule Apriori Algorithm and Markov Chain, a new method called the deviation rule Markov model is proposed. This model analyzes the correlation and transfer between Internet users’ emotion classes and predicts the changing trends of Internet users’ emotional states in the early stage of the pandemic by calculating the transfer probability of different emotion classes and constructing a time-varying emotion state transfer matrix. The experimental results demonstrated that the emotional state of netizens after the pandemic outbreak was not negative but gradually changed to “positive” emotions over time. Through a comparative analysis of examples, the validity and accuracy of the affective prediction model for online public opinion proposed in this study were verified.
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Received: 24 October 2022
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