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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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Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation., and . ACL (2), page 465-470. Association for Computational Linguistics, (2017)Email Classification with Temporal Features., , and . Intelligent Information Systems, page 523-533. Springer, (2004)Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010., , , , and . JAMIA, 18 (5): 557-562 (2011)Stance and Sentiment in Tweets., , and . CoRR, (2016)ExaCT: automatic extraction of clinical trial characteristics from journal publications., , , , and . BMC Med. Inf. & Decision Making, (2010)NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets., , and . SemEval@COLING, page 443-447. The Association for Computer Linguistics, (2014)Learning and Evaluation in the Presence of Class Hierarchies: Application to Text Categorization., , , and . Canadian Conference on AI, volume 4013 of Lecture Notes in Computer Science, page 395-406. Springer, (2006)Using Hashtags to Capture Fine Emotion Categories from Tweets., and . Computational Intelligence, 31 (2): 301-326 (2015)SemEval-2015 Task 10: Sentiment Analysis in Twitter., , , , , and . CoRR, (2019)The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition., and . WASSA@NAACL-HLT, page 43-52. The Association for Computer Linguistics, (2016)