Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts

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DOIResolve DOI: http://doi.org/10.1007/s10579-015-9328-1
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TypeArticle
Journal titleLanguage Resources and Evaluation
ISSN1574-020X
Volume50
Issue1
Pages3565; # of pages: 31
AbstractWe present the development and evaluation of a semantic analysis task that lies at the intersection of two very trendy lines of research in contemporary computational linguistics: (1) sentiment analysis, and (2) natural language processing of social media text. The task was part of SemEval, the International Workshop on Semantic Evaluation, a semantic evaluation forum previously known as SensEval. The task ran in 2013 and 2014, attracting the highest number of participating teams at SemEval in both years, and there is an ongoing edition in 2015. The task included the creation of a large contextual and message-level polarity corpus consisting of tweets, SMS messages, LiveJournal messages, and a special test set of sarcastic tweets. The evaluation attracted 44 teams in 2013 and 46 in 2014, who used a variety of approaches. The best teams were able to outperform several baselines by sizable margins with improvement across the 2 years the task has been run. We hope that the long-lasting role of this task and the accompanying datasets will be to serve as a test bed for comparing different approaches, thus facilitating research.
Publication date
PublisherSpringer International Publishing
LanguageEnglish
AffiliationNational Research Council Canada; Information and Communication Technologies
Peer reviewedYes
NPARC number21277502
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Record identifier88c97a2d-bed6-4055-912a-8af5ff4f19b2
Record created2016-03-09
Record modified2016-05-09
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