Multimodal Sarcasm Detection: A Deep Learning Approach

Abstract

In the modern era, posting sarcastic comments on social media became the common trend. Sarcasm is often used by people to taunt or pester others. It is frequently expressed through inflexion, tonal stress in speech or in the form of lexical, pragmatic, and hyperbolic features present in the text. Most of the existing work has been focused on either detecting sarcasm in textual data using text features or audio data using audio features. This article proposed a novel approach by combining textual and audio features together to detecting sarcasm in conversational data. This hybrid method takes a combined vector of extracted audio and text features from their respective models as the input. This combined features will compensated the shortcomings of only text features and vice-versa. The obtained result of hybrid model outperforms both the individual model significantly.