Finding Optimists and Pessimists on Twitter

Xianzhi Ruan, Steven Wilson, Rada Mihalcea

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Optimism is linked to various personal-ity factors as well as both psychologicaland physical health, but how does it re-late to the way a person tweets? We an-alyze the online activity of a set of Twit-ter users in order to determine how wellmachine learning algorithms can detect aperson’s outlook on life by reading theirtweets. A sample of tweets from each useris manually annotated in order to estab-lish ground truth labels, and classifiers aretrained to distinguish between optimisticand pessimistic users. Our results sug-gest that the words in people’s tweets pro-vide ample evidence to identify them asoptimists, pessimists, or somewhere in be-tween. Additionally, several applicationsof these trained models are explored.
Original languageEnglish
Title of host publicationThe 54th Annual Meeting of theAssociation for Computational Linguistics
Subtitle of host publicationProceedings of the Conference, Vol. 2 (Short Papers)
Place of PublicationBerlin, Germany
PublisherAssociation for Computational Linguistics
Pages320-325
Number of pages6
Volume2
ISBN (Electronic)978-1-945626-01-2
DOIs
Publication statusPublished - 12 Aug 2016
Event54th Annual Meeting of the Association for Computational Linguistics - Berlin, Germany
Duration: 7 Aug 201612 Aug 2016
https://mirror.aclweb.org/acl2016/

Conference

Conference54th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2016
Country/TerritoryGermany
CityBerlin
Period7/08/1612/08/16
Internet address

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