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Sentic Computing

A Common-Sense-Based Framework for Concept-Level Sentiment Analysis

  • Book
  • © 2015

Overview

Authors:
  1. Erik Cambria

    1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

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    You can also search for this author in PubMed   Google Scholar

  2. Amir Hussain

    1. Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom

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    You can also search for this author in PubMed   Google Scholar

  • First approach to sentiment analysis that merges AI, linguistics, and psychology
  • Comprehensive explanation of popular sentic computing techniques
  • Full set of linguistic patterns for sentiment analysis
  • Downloadable knowledge base

Part of the book series: Socio-Affective Computing (SAC, volume 1)

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Table of contents (5 chapters)

  1. Front Matter

    Pages i-xxii
    Download chapter PDF

  2. Introduction

    • Erik Cambria, Amir Hussain
    Pages 1-21

  3. SenticNet

    • Erik Cambria, Amir Hussain
    Pages 23-71

  4. Sentic Patterns

    • Erik Cambria, Amir Hussain
    Pages 73-106

  5. Sentic Applications

    • Erik Cambria, Amir Hussain
    Pages 107-153

  6. Conclusion

    • Erik Cambria, Amir Hussain
    Pages 155-160

  7. Back Matter

    Pages 161-176
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Keywords

About this book

This volume presents a knowledge-based approach to concept-level sentiment analysis at the crossroads between affective computing, information extraction, and common-sense computing, which exploits both computer and social sciences to better interpret and process information on the Web.
Concept-level sentiment analysis goes beyond a mere word-level analysis of text in order to enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.
 
Readers will discover the following key novelties, that make this approach so unique and avant-garde, being reviewed and discussed:
•    Sentic Computing's multi-disciplinary approach to sentiment analysis-evidenced by the concomitant use of AI, linguistics and psychology for knowledge representation and inference
•    Sentic Computing’s shift from syntax to semantics-enabled by the adoption of the bag-of-concepts model instead of simply counting word co-occurrence frequencies in text
•    Sentic Computing's shift from statistics to linguistics-implemented by allowing sentiments to flow from concept to concept based on the dependency relation between clauses

This volume is the first in the Series Socio-Affective Computing edited by Dr Amir Hussain and Dr Erik Cambria and will be of interest to researchers in the fields of socially intelligent, affective and multimodal human-machine interaction andsystems.

Authors and Affiliations

  • School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

  • Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom

    Amir Hussain

Bibliographic Information

  • Book Title: Sentic Computing

  • Book Subtitle: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis

  • Authors: Erik Cambria, Amir Hussain

  • Series Title: Socio-Affective Computing

  • DOI: https://doi.org/10.1007/978-3-319-23654-4

  • Publisher: Springer Cham

  • eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2015

  • Hardcover ISBN: 978-3-319-23653-7Published: 18 December 2015

  • Softcover ISBN: 978-3-319-79516-4Published: 21 March 2019

  • eBook ISBN: 978-3-319-23654-4Published: 11 December 2015

  • Series ISSN: 2509-5706

  • Series E-ISSN: 2509-5714

  • Edition Number: 1

  • Number of Pages: XXII, 176

  • Number of Illustrations: 14 b/w illustrations, 40 illustrations in colour

  • Topics: Neurosciences, Data Mining and Knowledge Discovery, Semantics, Cognitive Psychology

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