Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection

Taha Shangipour ataei, Soroush Javdan, Behrouz Minaei-Bidgoli
2020 Proceedings of the Second Workshop on Figurative Language Processing   unpublished
Sarcasm is a type of figurative language broadly adopted in social media and daily conversations. The sarcasm can ultimately alter the meaning of the sentence, which makes the opinion analysis process error-prone. In this paper, we propose to employ bidirectional encoder representations transformers (BERT), and aspect-based sentiment analysis approaches in order to extract the relation between context dialogue sequence and response and determine whether or not the response is sarcastic. The
more » ... sarcastic. The best performing method of ours obtains an F1 score of 0.73 on the Twitter dataset and 0.734 over the Reddit dataset at the second workshop on figurative language processing Shared Task 2020.
doi:10.18653/v1/2020.figlang-1.9 fatcat:2suvbhiq3nehbiyqftse4xjpbm