Sarcasm Detection: A Comparative Study

• URL: http://arxiv.org/abs/2107.02276v2
• Date: Wed, 7 Jul 2021 02:07:19 GMT
• Title: Sarcasm Detection: A Comparative Study
• Authors: Hamed Yaghoobian, Hamid R. Arabnia, Khaled Rasheed
• Abstract summary: Sarcasm detection is the task of identifying irony containing utterances in sentiment-bearing text. This article compiles and reviews the salient work in the literature of automatic sarcasm detection.
• Score: 1.7725414095035827
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sarcasm detection is the task of identifying irony containing utterances in sentiment-bearing text. However, the figurative and creative nature of sarcasm poses a great challenge for affective computing systems performing sentiment analysis. This article compiles and reviews the salient work in the literature of automatic sarcasm detection. Thus far, three main paradigm shifts have occurred in the way researchers have approached this task: 1) semi-supervised pattern extraction to identify implicit sentiment, 2) use of hashtag-based supervision, and 3) incorporation of context beyond target text. In this article, we provide a comprehensive review of the datasets, approaches, trends, and issues in sarcasm and irony detection.

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