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

2021; Volume: 5; Issue: 1 Linguagem: Inglês

10.1609/icwsm.v5i1.14171

2334-0770

Johan Bollen, Huina Mao, Alberto Pepe,

Opinion Dynamics and Social Influence

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.