Sarcasm Detection Framework Using Emotion and Sentiment Features

• URL: http://arxiv.org/abs/2211.13014v1
• Date: Wed, 23 Nov 2022 15:14:44 GMT
• Title: Sarcasm Detection Framework Using Emotion and Sentiment Features
• Authors: Oxana Vitman, Yevhen Kostiuk, Grigori Sidorov, Alexander Gelbukh
• Abstract summary: We propose a model which incorporates emotion and sentiment features to capture the incongruity intrinsic to sarcasm. Our approach achieved state-of-the-art results on four datasets from social networking platforms and online media.
• Score: 62.997667081978825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sarcasm detection is an essential task that can help identify the actual sentiment in user-generated data, such as discussion forums or tweets. Sarcasm is a sophisticated form of linguistic expression because its surface meaning usually contradicts its inner, deeper meaning. Such incongruity is the essential component of sarcasm, however, it makes sarcasm detection quite a challenging task. In this paper, we propose a model which incorporates emotion and sentiment features to capture the incongruity intrinsic to sarcasm. Moreover, we use CNN and pre-trained Transformer to capture context features. Our approach achieved state-of-the-art results on four datasets from social networking platforms and online media.

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