A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict

Anonymous

Abstract: Sarcasm employs ambivalence, where one says something positive but actually means negative, vice versa. The essence of sarcasm, which is also a sufficient and necessary condition, is conflict between the literal and implied sentiments expressed in one sentence. However, it is difficult to recognize such sentiment conflict because of the sentiments are mixed or even implicit.As a result, the recognition of sophisticated and obscure sentiment brings in a great challenge to sarcasm detection. In this paper, we propose a Dual-Channel Framework by modeling both literal and implied sentiments separately. Based on this dual-channel framework, we design the Dual-Channel Net~(DC-Net) to recognize sentiment conflict.Experiments on political debates (\ie IAC-V1 and IAC-V2) and Twitter datasets show that our proposed DC-Net achieves state-of-the-art performance on sarcasm recognition.

Paper Type: long