Multi-Modal Sarcasm Detection in Social Networks: A Comparative Review

Sentiment Analysis (SA) has become an extremely sought after area of research especially post COVID-19 when people used to spent a lot of time on the social media to interact with each other. This interaction was done through posts having both textual and visual cues and also by participating in online discussions forums. Some of the inherent challenges encountered in the process of SA include discernment of sarcasm, irony, humor, negation, multi-polarity or Aspect-Level Sentiment Analysis (ASA) etc. Researchers are now gradually shifting their focus to the identification and detection of sarcasm and how it can empower SA. Sarcasm expresses a person’s downside feelings by using positive words in an implicit way. It also has an overall impact on increasing the efficiency of the SA models. Eliciting sarcastic statements is tough for humans as well as for machines without the knowledge of the context or background in which it is expressed, body language and/or facial expression of the speaker and his voice modulation. This review paper studies some of the approaches used for sarcasm detection and also guides researchers in exploring the different modalities of data for developing applications like a virtual chat-bot or assistant, depression analysis, stress management system at workplace etc.