@inproceedings{Bollen,
	title = {Modeling {Public} {Mood} and {Emotion}: {Twitter} {Sentiment} and {Socio}-{Economic} {Phenomena}},
	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 ex-tract six mood states (tension, depression, anger, vigor, fatigue, confusion) from the aggregated Twitter con-tent 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, cul-tural and economic sphere do have a significant, imme-diate and highly specific effect on the various dimen-sions of public mood. We speculate that large scale anal-yses of mood can provide a solid platform to model col-lective emotive trends in terms of their predictive value with regards to existing social as well as economic indi-cators.},
	urldate = {2017-08-17},
	author = {Bollen, Johan and Mao, Huina and Pepe, Alberto},
	keywords = {Poster Papers, microblogging, public emotion, public mood, sentiment analysis, social media, socio-economic analysis},
}

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