abstract: Sarcasm is a nuanced form of language where usually, the speaker explicitly states the opposite of what is implied. Imbued with intentional ambiguity and subtlety, detecting sarcasm is a difficult task, even for humans. Current works approach this challenging problem primarily from a linguistic perspective, focusing on the lexical and syntactic aspects of sarcasm. In this thesis, I explore the possibility of using behavior traits intrinsic to users of sarcasm to detect sarcastic tweets. First, I theorize the core forms of sarcasm using findings from the psychological and behavioral sciences, and some observations on Twitter users. Then, I develop computational features to model the manifestations of these forms of sarcasm using the user's profile information and tweets. Finally, I combine these features to train a supervised learning model to detect sarcastic tweets. I perform experiments to extensively evaluate the proposed behavior modeling approach and compare with the state-of-the-art. / Dissertation/Thesis / Masters Thesis Computer Science 2014
| Identifer | oai:union.ndltd.org:asu.edu/item:26799 |
| Date | January 2014 |
| Contributors | Rajadesingan, Ashwin (Author), Liu, Huan (Advisor), Kambhampati, Subbarao (Committee member), Pon-Barry, Heather (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 59 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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