Deep Learning techniques for Sarcasm Detection

Abstract:
Sarcasm is a literary technique used to expose the shortcomings or contradictions of the object. It frequently appears on various online platforms (e.g., Twitter and Facebook), expressing the subjective views and opinions of the sender of the behavior. Sarcasm detection is very beneficial to various online platforms, which help realize sentiment analysis, opinion mining and advertisement push. Therefore, the research on sarcasm detection has reached a peak contemporarily. With the development of deep learning and the successful application of network platforms in natural language processing, in recent years, some studies have applied deep learning to address sarcasm detection tasks. These studies put forward new models based on techniques (e.g., convolutional neural networks and Transformers), which were tested by a large number of experiments to explore the performance of the models. The model proposed by the research is compared with the classic model and continuously improved. This paper reviews some previous work based on convolutional neural networks, transformers and other networks, lists important factors (e.g., data sets and matrices), introduces some research frameworks and solutions, and gives an evaluation of classification model index. The work done in this article will provide a good reference for later researchers.