We process social media data, e.g., Twitter data, for analyzing human behavior.
Sentiment Analysis
We develop computational approaches to classify sentiments into five categories: very negative, negative, neutral, positive, and very positive. We also conduct sentiment analysis of social media data to analyze human behavior.
Citation
- Meng Hsiu Tsai and Yingfeng Wang*, “Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19,” International Journal of Environmental Research and Public Health, vol. 18, no. 12, pp. 6272, 2021.
- Meng Hsiu Tsai and Yingfeng Wang*, “A New Ensemble Method for Classifying Sentiments of COVID-19-Related Tweets,” Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI), pp. 313-316, 2020.
- Meng-Hsiu Tsai, Yingfeng Wang*, Myungjae Kwak, and Neil Rigole, “A Machine Learning Based Strategy for Election Result Prediction,” Proceedings of International Conference on Computational Science and Computational Intelligence (CSCI), pp 1408-1410, Dec. 2019.
Information Diffusion Analysis
We analyze the true and false information diffusion on Twitter data.
Citation
- Amin Riazi and Yingfeng Wang*, “Using Topological Analysis to Investigate True and False Information Diffusion,” Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI), accepted.