Modelling Propagation of Public Opinions on Microblogging Big Data Using Sentiment Analysis and Compartmental Models

Youjia Fang, Xin Chen, Zheng Song, Tianzi Wang and Yang Cao
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Youjia Fang: Virginia Tech Transportation Institute, Virginia Tech, Blacksburg, VA, USA
Xin Chen: Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
Zheng Song: Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
Tianzi Wang: Department of Industrial & Systems Engineering, Virginia Tech, Blacksburg, VA, USA
Yang Cao: Department of Computer Science, Virginia Tech, Blacksburg, VA, USA

International Journal on Semantic Web and Information Systems (IJSWIS), 2017, vol. 13, issue 1, 11-27

Abstract: Compartmental models have been used to model information diffusion on social media. However, there have been few studies on modelling positive and negative public opinions using compartmental models. This study aimed for using sentiment analysis and compartmental model to model the propagation of positive and negative opinions on microblogging big media. The authors studied the news propagation of seven popular social topics on China's Sina Weibo microblogging platform. Natural language processing and sentiment analysis were used to identify public opinions from microblogging big data. Then two existing (SIZ and SEIZ) models and a newly developed (SE2IZ) model were implemented to model the news propagation and evaluate the trends of public opinions on selected social topics. Simulation study was used to check model fitting performance. The results show that the new SE2IZ model has a better model fitting performance than existing models. This study sheds some new light on using social media for public opinion estimation and prediction.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jswis0:v:13:y:2017:i:1:p:11-27

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