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

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arxiv 2005.11882 v2 pith:LIDX6YEJ submitted 2020-05-25 cs.CL

classification cs.CL
keywords analysisemotionssentimenttextbehaviourdiscussresearchsocial
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 20 citations worldwide. Full citation record

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    LLM-based scoring of 1,610 therapy sessions finds therapist empathy and exploration are followed by more client disclosure, while prior-session rapport is associated with less self-directed negative emotion—but the cl...

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