Ameeta Agrawal
ABSTRACT
One popular thread of research in computational sarcasm detection involves modeling sarcasm as a contrast between positive and negative sentiment polarities or exploring more fine-grained categories of emotions such as happiness, sadness, surprise, and so on. Most current models, however, treat these affective features independently, without regard for the sequential information encoded among the affective states. In order to explore the role of transitions in affective states, we formulate the task of sarcasm detection as a sequence classification problem by leveraging the natural shifts in various emotions over the course of a piece of text. Experiments conducted on datasets from two different genres suggest that our proposed approach particularly benefits datasets with limited labeled data and longer instances of text.
Supplemental Material
- Ameeta Agrawal and Aijun An. 2018. Affective representations for sarcasm detection. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 1029--1032.Google Scholar
Digital Library
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Index Terms
- Leveraging Transitions of Emotions for Sarcasm Detection
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