Socio-Affective Computing

Prominent Feature Extraction for Sentiment Analysis

Authors: Agarwal, Basant, Mittal, Namita

  • Includes a novel semantic parsing scheme which may be applied to many Natural language processing tasks
  • ProvidesĀ an efficient machine learning approach for sentiment analysis
  • Easy to understand and deployable
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eBook $119.00
price for USA (gross)
  • ISBN 978-3-319-25343-5
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $159.99
price for USA
  • ISBN 978-3-319-25341-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

The objective of this monograph is to improve the performance of the sentiment analysis model by incorporating the semantic, syntactic and common-sense knowledge. This book proposes a novel semantic concept extraction approach that uses dependency relations between words to extract the features from the text. Proposed approach combines the semantic and common-sense knowledge for the better understanding of the text. In addition, the book aims to extract prominent features from the unstructured text by eliminating the noisy, irrelevant and redundant features. Readers will also discover a proposed method for efficient dimensionality reduction to alleviate the data sparseness problem being faced by machine learning model.

Authors pay attention to the four main findings of the book :
-Performance of the sentiment analysis can be improved by reducing the redundancy among the features. Experimental results show that minimum Redundancy Maximum Relevance (mRMR) feature selection technique improves the performance of the sentiment analysis by eliminating the redundant features.
- Boolean Multinomial Naive Bayes (BMNB) machine learning algorithm with mRMR feature selection technique performs better than Support Vector Machine (SVM) classifier for sentiment analysis.
- The problem of data sparseness is alleviated by semantic clustering of features, which in turn improves the performance of the sentiment analysis.

- Semantic relations among the words in the text have useful cues for sentiment analysis. Common-sense knowledge in form of ConceptNet ontology acquires knowledge, which provides a better understanding of the text that improves the performance of the sentiment analysis.

Table of contents (7 chapters)

  • Introduction

    Agarwal, Basant (et al.)

    Pages 1-4

  • Literature Survey

    Agarwal, Basant (et al.)

    Pages 5-19

  • Machine Learning Approach for Sentiment Analysis

    Agarwal, Basant (et al.)

    Pages 21-45

  • Semantic Parsing Using Dependency Rules

    Agarwal, Basant (et al.)

    Pages 47-61

  • Sentiment Analysis Using ConceptNet Ontology and Context Information

    Agarwal, Basant (et al.)

    Pages 63-75

Buy this book

eBook $119.00
price for USA (gross)
  • ISBN 978-3-319-25343-5
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $159.99
price for USA
  • ISBN 978-3-319-25341-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Prominent Feature Extraction for Sentiment Analysis
Authors
Series Title
Socio-Affective Computing
Series Volume
2
Copyright
2016
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing Switzerland
eBook ISBN
978-3-319-25343-5
DOI
10.1007/978-3-319-25343-5
Hardcover ISBN
978-3-319-25341-1
Series ISSN
2509-5706
Edition Number
1
Number of Pages
XIX, 103
Number of Illustrations and Tables
8 b/w illustrations, 2 illustrations in colour
Topics