Sarcasm Detection: A Comparative Study
It provides a comprehensive review for researchers in affective computing, but is incremental as it summarizes existing work without introducing novel methods.
This paper tackles the problem of sarcasm detection in sentiment analysis by reviewing existing literature, identifying three main paradigm shifts in approaches, but does not present new results or concrete numbers.
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.