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

TLDR

The study proposes that large‑scale mood analysis can predict social and economic trends. The authors analyzed all tweets from the second half of 2008, extracted six daily mood dimensions with a psychometric instrument, and compared these mood vectors to a record of major events. They found that social, political, cultural, and economic events produce significant, immediate, and specific shifts in public mood.

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.