Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena

Authors

  • Johan Bollen Indiana University
  • Huina Mao Indiana University
  • Alberto Pepe Harvard University

Abstract

We perform a sentiment analysis of all tweets published on the microblogging platform Twitter in the second half of 2008. We use a psychometric instrument to extract six mood states (tension, depression, anger, vigor, fatigue, confusion) from the aggregated Twitter content and compute a six-dimensional mood vector for each day in the timeline. We compare our results to a record of popular events gathered from media and sources. We find that events in the social, political, cultural and economic sphere do have a significant, immediate and highly specific effect on the various dimensions of public mood. We speculate that large scale analyses of mood can provide a solid platform to model collective emotive trends in terms of their predictive value with regards to existing social as well as economic indicators.

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Published

2021-08-03

How to Cite

Bollen, J., Mao, H., & Pepe, A. (2021). Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 450-453. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14171