Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text

TL;DR

This paper provides a comprehensive overview of sentiment analysis, covering its history, methods, challenges, applications, and ethical considerations, highlighting recent research efforts towards fairness.

Contribution

It offers an extensive survey of sentiment analysis research, including tasks, methods, resources, and ethical issues, serving as a foundational reference for future work.

Findings

Sentiment analysis enables insights in commerce, health, and social sciences.

Recent research emphasizes fairness and ethical considerations.

The field faces challenges in task diversity and resource availability.

Abstract

Recent advances in machine learning have led to computer systems that are human-like in behaviour. Sentiment analysis, the automatic determination of emotions in text, is allowing us to capitalize on substantial previously unattainable opportunities in commerce, public health, government policy, social sciences, and art. Further, analysis of emotions in text, from news to social media posts, is improving our understanding of not just how people convey emotions through language but also how emotions shape our behaviour. This article presents a sweeping overview of sentiment analysis research that includes: the origins of the field, the rich landscape of tasks, challenges, a survey of the methods and resources used, and applications. We also discuss discuss how, without careful fore-thought, sentiment analysis has the potential for harmful outcomes. We outline the latest lines of research in pursuit of fairness in sentiment analysis.