Semi-Supervised Recognition of Sarcasm in Twitter and Amazon

By Dmitry Davidov, Oren Tsur, Ari Rappoport. Conference on Computational Natural Language Learning, 2010

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SemEval-2013 Task 2: Sentiment Analysis in Twitter (2013, background): “…the opinions and sentiments that people are communicating has only very recently been the subject of research (Jansen et al., 2009; Barbosa and Feng, 2010; Bifet et al., 2011; Davidov et al., 2010; O’Connor et al., 2010; Pak and Paroubek, 2010; Tumasjan et al., 2010; Kouloumpis et al., 2011).”
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, background): “[31], and predominantly by certain types of Twitter users, Bamman & Smith [5]).”
What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities (2012, background): “Sentiment analysis of Twitter data is studied in many works, [8, 9] among others.”
Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis. (2014, methodology): “Davidov et al. (Davidov et al., 2010) use Tsur’s algorithm for sarcasm detection, and achieve 82.7% F1 on tweets and 78.8% on Amazon reviews.”