Automatic Sarcasm Detection: A Survey
Aditya Joshi, Pushpak Bhattacharyya, Mark James Carman

TL;DR
This survey comprehensively reviews the evolution, datasets, methods, and challenges in automatic sarcasm detection, highlighting key milestones and future directions in the field.
Contribution
It is the first compilation of past work in sarcasm detection, summarizing approaches, datasets, trends, and issues, and providing a resource for future research.
Findings
Identification of three research milestones: implicit sentiment, hashtag supervision, context use.
Summary of datasets, approaches, and performance trends in sarcasm detection.
Discussion of future challenges and directions in the field.
Abstract
Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Beginning with an approach that used speech-based features, sarcasm detection has witnessed great interest from the sentiment analysis community. This paper is the first known compilation of past work in automatic sarcasm detection. We observe three milestones in the research so far: semi-supervised pattern extraction to identify implicit sentiment, use of hashtag-based supervision, and use of context beyond target text. In this paper, we describe datasets, approaches, trends and issues in sarcasm detection. We also discuss representative performance values, shared tasks and pointers to future work, as given in prior works. In terms of resources that could be useful for understanding…
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Taxonomy
TopicsIdentification and Quantification in Food · Forensic Entomology and Diptera Studies · Forensic and Genetic Research
