Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Text
It addresses the problem of automatically detecting emotions in text for applications in commerce, public health, and social sciences, but is incremental as it is a survey.
This article provides a comprehensive overview of sentiment analysis research, covering its origins, tasks, methods, resources, and applications, while also addressing potential harmful outcomes and fairness issues.
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.