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The SenticNet Sentiment Lexicon: Exploring Semantic Richness in Multi-Word Concepts / by Raoul Biagioni

By: Biagioni, Raoul
Contributor(s): SpringerLink (Online service)
Material type: materialTypeLabelE-bookSeries: SpringerBriefs in Cognitive Computation; 44Publisher: Cham : Springer International Publishing, 2016Description: 1 recurso en línea (VI, 55 p.) : 13 ilustraciones, 8 ilustraciones en color.ISBN: 9783319389714.Subject: Medicina | NeurocienciasDDC classification: 612.8 Online resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
Introduction -- Sentiment Analysis -- SenticNet -- Unsupervised Sentiment Classification -- Evaluation -- Conclusion -- Index. .
Summary: 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.
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias Sociales P325.5.D38 B534 2016 EB (Browse shelf(Opens below)) .i11596284 Acceso electrónico eBOOK .i11596284
Total holds: 0

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.

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