Sarcasm Detection with Commonsense Knowledge

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Sarcasm Detection with Commonsense Knowledge

Jiangnan Li; Hongliang Pan; Zheng Lin; Peng Fu; Weiping Wang

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.

Sarcasm is a form of figurative language, defined as “the use of irony to mock or convey contempt”1, which is ubiquitous in social media platforms such as Twitter and Reddit. People tend to use sarcasm to express the opposite of superficial meaning [1]. The utterance “I love to see a doctor every day” expresses sarcastic meaning. It shows a negative sentiment towards the situation of “see a doctor every day”, even the utterance contains positive sentiment words such as “like”. 

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