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SemEval-2015 Task 10: Sentiment Analysis in Twitter.

, , , , , and . SemEval@NAACL-HLT, page 451-463. The Association for Computer Linguistics, (2015)

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The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition., and . WASSA@NAACL-HLT, page 43-52. The Association for Computer Linguistics, (2016)SemEval-2018 Task 1: Affect in Tweets., , , and . SemEval@NAACL-HLT, page 1-17. Association for Computational Linguistics, (2018)DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning., , , , and . SemEval@NAACL-HLT, page 305-312. Association for Computational Linguistics, (2018)Sentiment after Translation: A Case-Study on Arabic Social Media Posts., , and . HLT-NAACL, page 767-777. The Association for Computational Linguistics, (2015)Detecting Stance in Tweets And Analyzing its Interaction with Sentiment., , and . *SEM@ACL, The *SEM 2016 Organizing Committee, (2016)Email classification with co-training., and . CASCON, page 8. IBM, (2001)Happy Accident: A Sentiment Composition Lexicon for Opposing Polarity Phrases., and . LREC, European Language Resources Association (ELRA), (2016)Sentiment Lexicons for Arabic Social Media., , and . LREC, European Language Resources Association (ELRA), (2016)Quantifying Qualitative Data for Understanding Controversial Issues., , , and . LREC, European Language Resources Association (ELRA), (2018)Using Hashtags to Capture Fine Emotion Categories from Tweets., and . Computational Intelligence, 31 (2): 301-326 (2015)