A Prior Probability of Speaker Information and Emojis Embedding Approach to Sarcasm Detection
Yin Wang, XU Xu-yang, Ziteng Gao, Xi Shi · 2021
Sarcasm language is widely presented in social media and student chats. This is not only a difficult sentiment analysis task, but also the basis for completing other follow-up tasks. However, most of the previous methods are based on complex network model design, which leads to poor traceability of inference results. Or based on the non-corresponding pre-training model, which is not taking advantage of the area where each model can do better. We propose a DialoGPT model based on pre-training on the sarcasm task, combined with the information of the sarcasm probability about the speaker, and embedded emojis in sentences to enhance recognition. The algorithm we designed is tested on the dataset named sarcasm on reddit and compared with other related methods and systems. The results show that the proposed method achieves the most advanced performance in the four indicators in basically the same time. F1-score has achieved 77, which is better than other methods, and it uses the advantages of new information content. In the field of education, the method we proposed can be used to help understand and judge students' intention and personality.