Emotion Detection in Text: a Review

TL;DR

This review discusses the advancements and challenges in emotion detection in text, emphasizing the need for improved methods to handle the complexity and subtlety of human emotional expression.

Contribution

It provides a comprehensive overview of existing techniques and highlights the limitations and future directions for emotion detection in textual data.

Findings

Many techniques exist but are insufficient for complex emotions

Implicit and metaphorical language pose challenges

Improving system design requires understanding linguistic intricacies

Abstract

In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc. Access to a huge amount of textual data, especially opinionated and self-expression text also played a special role to bring attention to this field. In this paper, we review the work that has been done in identifying emotion expressions in text and argue that although many techniques, methodologies, and models have been created to detect emotion in text, there are various reasons that make these methods insufficient. Although, there is an essential need to improve the design and architecture of current systems, factors such as the complexity of human emotions, and the use of implicit and metaphorical language in expressing it, lead us to think that just re-purposing standard methodologies will not be enough to capture these complexities, and it is important to pay attention to the linguistic intricacies of emotion expression.