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

We perform a sentiment analysis of all tweets published on the microblogging platform Twitter in the second half of 2008. 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.

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• 1 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.
• 2 We compare our results to a record of popular events gathered from media and sources.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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