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Sentic Computing

Techniques, Tools, and Applications

  • Book
  • © 2012

Overview

Authors:
  1. Erik Cambria

    1. , Media Laboratory, Massachusetts Institute of Technology, Cambridge, USA

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  2. Amir Hussain

    1. Dept. Computing Science, University of Stirling, Stirling, United Kingdom

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    You can also search for this author in PubMed   Google Scholar

  • Represents the first comprehensive review of Sentic Computing, state-of-the-art approach to opinion mining and sentiment analysis (see http://en.wikipedia.org/wiki/Sentiment_analysis)
  • A special chapter on cognitive and affective modeling for natural language understanding
  • Includes tips on different strategies (techniques, online resources, datasets, etc.) to opinion mining and sentiment analysis
  • Includes supplementary material: sn.pub/extras
  • Includes supplementary material: sn.pub/extras

Part of the book series: SpringerBriefs in Cognitive Computation (BRIEFSCC, volume 2)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Introduction

    • Erik Cambria, Amir Hussain
    Pages 1-10

  3. Background

    • Erik Cambria, Amir Hussain
    Pages 11-33

  4. Techniques

    • Erik Cambria, Amir Hussain
    Pages 35-67

  5. Tools

    • Erik Cambria, Amir Hussain
    Pages 69-101

  6. Applications

    • Erik Cambria, Amir Hussain
    Pages 103-146

  7. Concluding Remarks

    • Erik Cambria, Amir Hussain
    Pages 147-153
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Keywords

About this book

In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques is exploited on two common sense knowledge bases to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data.

Authors and Affiliations

  • , Media Laboratory, Massachusetts Institute of Technology, Cambridge, USA

    Erik Cambria

  • Dept. Computing Science, University of Stirling, Stirling, United Kingdom

    Amir Hussain

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