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The SenticNet sentiment lexiconexplo...

Biagioni, Raoul.

The SenticNet sentiment lexiconexploring semantic richness in multi-word concepts /

Record Type:	Electronic resources : Monograph/item
Title/Author:	The SenticNet sentiment lexiconby Raoul Biagioni.
Reminder of title:	exploring semantic richness in multi-word concepts /
Author:	Biagioni, Raoul.
Published:	Cham :Springer International Publishing :2016.
Description:	vi, 55 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	SemanticsData processing.
Online resource:	http://dx.doi.org/10.1007/978-3-319-38971-4
ISBN:	9783319389714$q(electronic bk.)

The SenticNet sentiment lexiconexploring semantic richness in multi-word concepts /
Biagioni, Raoul.

The SenticNet sentiment lexiconexploring semantic richness in multi-word concepts /[electronic resource] :by Raoul Biagioni. - Cham :Springer International Publishing :2016. - vi, 55 p. :ill., digital ;24 cm. - SpringerBriefs in cognitive computation,v.42212-6023 ;. - SpringerBriefs in cognitive computation ;2..

Introduction -- Sentiment Analysis -- SenticNet -- Unsupervised Sentiment Classification -- Evaluation -- Conclusion -- Index.

The research and its outcomes presented in this book, is about lexicon-based sentiment analysis. It uses single-, and multi-word concepts from the SenticNet sentiment lexicon as the source of sentiment information for the purpose of sentiment classification. In 6 chapters the book sheds light on the comparison of sentiment classification accuracy between single-word and multi-word concepts, for which a bespoke sentiment analysis system developed by the author was used. This book will be of interest to students, educators and researchers in the field of Sentic Computing.

ISBN: 9783319389714$q(electronic bk.)

Standard No.: 10.1007/978-3-319-38971-4doiSubjects--Topical Terms:

217440
Semantics
--Data processing.

LC Class. No.: P325.5.D38 / B53 2016

Dewey Class. No.: 006.35

The SenticNet sentiment lexiconexploring semantic richness in multi-word concepts /
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505 0 $a Introduction -- Sentiment Analysis -- SenticNet -- Unsupervised Sentiment Classification -- Evaluation -- Conclusion -- Index.
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