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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text
Creator	Mohammad, Saif
source	ArXiv
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.
has issue date	2020-05-25(xsd:dateTime)
has license	arxiv
sha1sum (hex)	11c51eee0040547cfa8d49a4cc753d00d30beb98
resource representing a document's title	Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text
resource representing a document's body	covid:11c51eee0040547cfa8d49a4cc753d00d30beb98#body_text
is schema:about of	named entity 'Further' named entity 'automatic' named entity 'landscape' named entity 'determination' named entity 'policy' »more»

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