Overview
- Presents statistical analysis of SenticNet that has not been produced before
- Offers original research into concept-level and knowledge-based sentiment analysis using the SenticNet sentiment lexicon
- Broadens understanding of sentiment analysis
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Cognitive Computation (BRIEFSCC, volume 4)
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Table of contents (6 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.Authors and Affiliations
Bibliographic Information
Book Title: The SenticNet Sentiment Lexicon: Exploring Semantic Richness in Multi-Word Concepts
Authors: Raoul Biagioni
Series Title: SpringerBriefs in Cognitive Computation
DOI: https://doi.org/10.1007/978-3-319-38971-4
Publisher: Springer Cham
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: The Author(s) 2016
Softcover ISBN: 978-3-319-38970-7Published: 06 June 2016
eBook ISBN: 978-3-319-38971-4Published: 28 May 2016
Series ISSN: 2212-6023
Series E-ISSN: 2212-6031
Edition Number: 1
Number of Pages: VI, 55
Number of Illustrations: 5 b/w illustrations, 8 illustrations in colour
Topics: Neurosciences, Natural Language Processing (NLP), Semantics