Prominent Feature Extraction for Sentiment Analysis

  • Basant Agarwal
  • Namita Mittal

Part of the Socio-Affective Computing book series (SAC)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Basant Agarwal, Namita Mittal
    Pages 1-4
  3. Basant Agarwal, Namita Mittal
    Pages 5-19
  4. Basant Agarwal, Namita Mittal
    Pages 21-45
  5. Basant Agarwal, Namita Mittal
    Pages 47-61
  6. Basant Agarwal, Namita Mittal
    Pages 77-88
  7. Basant Agarwal, Namita Mittal
    Pages 89-92
  8. Back Matter
    Pages 93-103

About this book

Introduction

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.


Keywords

Machine Learning Minimum Redundancy and Maximum Relevance feature selection Prominent Feature Extraction Semantic Parser Sentiment Analysis

Authors and affiliations

  • Basant Agarwal
    • 1
  • Namita Mittal
    • 2
  1. 1.Computer Science and EngineeringMalaviya National Institute of TechnologyJaipurIndia
  2. 2.Computer Science and EngineeringMalaviya National Institute of TechnologyJaipurIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-25343-5
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Biomedical and Life Sciences
  • Print ISBN 978-3-319-25341-1
  • Online ISBN 978-3-319-25343-5
  • Series Print ISSN 2509-5706
  • Series Online ISSN 2509-5714
  • About this book
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