A Technical Review on Emotion Detection from Informal Text for Indian Regional Languages: Approaches and Challenges

Abstract

Effective communication is a crucial element in the existence of a human being. Emotions are a common component of human communication and can appear in a variety of ways, including spoken words, written messages, and non-verbal indicators like gestures and facial expressions. Textual communication which is the common way of interaction has been rapidly increasing day-by-day. People nowadays are using social media to convey their thoughts and beliefs in the form of comments, posts and stories with associated emotions. As a result, there is a growing need to detect and understand the emotions conveyed in texts. Although the emotions expressed in text are readily understood by the human brain, teaching a machine to do the same is a challenging task. However, when dealing with Indian languages, this process becomes especially difficult. As India has plenty of vernacular languages, people opt to use monolingual or code-mix languages. These types of languages are generally informal way which creates an obstacle to convey the emotions. The shortage of annotated corpus especially for regional languages is also one of the challenges. This paper aims to analyze and review emotion detection from textual data specifically for Indian regional languages and its challenges.