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Sarcasm Detection with Commonsense Knowledge
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AI
DOI：10.1109/TASLP.2021.3120601.png)
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Abstract
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Sarcasm is commonly used in today's social media platforms such as Twitter and Reddit. Sarcasm detection is necessary for analysing people's real sentiments as people usually use sarcasm to express a flipped emotion against the literal meaning. However, the current works neglect the fact that commonsense knowledge is crucial for sarcasm recognition. In this paper, we propose a novel architecture in deep learning for sarcasm detection by integrating commonsense knowledge. To be specific, we apply the pre-trained COMET model to generate relevant commonsense knowledge. Besides, we compare two kinds of knowledge selection strategies to investigate how commonsense knowledge influences performance. Finally, a knowledge-text integration module is designed to model both text and knowledge. The experimental results demonstrate our model's effectiveness on three datasets, including two Twitter datasets and a Reddit dataset.
Keywords:
Comets
Social networking (online)
Speech processing
Bit error rate
Medical services
Task analysis
Encoding
Commonsense knowledge
sarcasm detection
deep learning
knowledge selection
knowledge-text integration
Journal
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5.1
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2.6K
Citations:
1.1W
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