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A Practical Guide to Sentiment AnalysisConcept-Level Sentiment Analysis with SenticNet

A Practical Guide to Sentiment Analysis: Concept-Level Sentiment Analysis with SenticNet [SenticNet is a publicly available resource for opinion mining that exploits AI, linguistics, and psychology to infer the polarity associated with commonsense concepts and encode this in a semantic-aware representation. In particular, SenticNet uses dimensionality reduction to calculate the affective valence of multi-word expressions and, hence, represent it in a machine-accessible and machine-processable format. This chapter presents an overview of the most recent sentic computing tools and techniques, with particular focus on applications in the context of big social data analysis.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Practical Guide to Sentiment AnalysisConcept-Level Sentiment Analysis with SenticNet

Part of the Socio-Affective Computing Book Series (volume 5)
Editors: Cambria, Erik; Das, Dipankar; Bandyopadhyay, Sivaji; Feraco, Antonio

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References (50)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing AG 2017. Chapter 4 is published with kind permission of the Her Majesty the Queen Right of Canada.
ISBN
978-3-319-55392-4
Pages
173 –188
DOI
10.1007/978-3-319-55394-8_9
Publisher site
See Chapter on Publisher Site

Abstract

[SenticNet is a publicly available resource for opinion mining that exploits AI, linguistics, and psychology to infer the polarity associated with commonsense concepts and encode this in a semantic-aware representation. In particular, SenticNet uses dimensionality reduction to calculate the affective valence of multi-word expressions and, hence, represent it in a machine-accessible and machine-processable format. This chapter presents an overview of the most recent sentic computing tools and techniques, with particular focus on applications in the context of big social data analysis.]

Published: Apr 12, 2017

Keywords: SenticNet; Sentic computing; Concept-level sentiment analysis; Big social data analysis

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