A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions

TL;DR

This paper systematically reviews the evolution, techniques, datasets, applications, and future challenges of text-based emotion detection (TBED) from 2005 to 2021, highlighting its growing importance in AI-human interaction.

Contribution

It provides a comprehensive overview of TBED research, including models, methods, datasets, applications, and future research directions, based on an analysis of 63 studies.

Findings

TBED has evolved significantly from 2005 to 2021.

Various emotion models and feature extraction techniques are used in TBED.

Future challenges include dataset diversity and model interpretability.

Abstract

Artificial Intelligence (AI) has been used for processing data to make decisions, interact with humans, and understand their feelings and emotions. With the advent of the internet, people share and express their thoughts on day-to-day activities and global and local events through text messaging applications. Hence, it is essential for machines to understand emotions in opinions, feedback, and textual dialogues to provide emotionally aware responses to users in today's online world. The field of text-based emotion detection (TBED) is advancing to provide automated solutions to various applications, such as businesses, and finances, to name a few. TBED has gained a lot of attention in recent times. The paper presents a systematic literature review of the existing literature published between 2005 to 2021 in TBED. This review has meticulously examined 63 research papers from IEEE, Science Direct, Scopus, and Web of Science databases to address four primary research questions. It also reviews the different applications of TBED across various research domains and highlights its use. An overview of various emotion models, techniques, feature extraction methods, datasets, and research challenges with future directions has also been represented.