Authors:
- Includes a novel semantic parsing scheme which may be applied to many Natural language processing tasks
- Provides an efficient machine learning approach for sentiment analysis
- Easy to understand and deployable
Part of the book series: Socio-Affective Computing (SAC)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
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.
Authors and Affiliations
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Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India
Basant Agarwal, Namita Mittal
Bibliographic Information
Book Title: Prominent Feature Extraction for Sentiment Analysis
Authors: Basant Agarwal, Namita Mittal
Series Title: Socio-Affective Computing
DOI: https://doi.org/10.1007/978-3-319-25343-5
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 2016
Hardcover ISBN: 978-3-319-25341-1Published: 18 December 2015
Softcover ISBN: 978-3-319-79775-5Published: 28 March 2019
eBook ISBN: 978-3-319-25343-5Published: 14 December 2015
Series ISSN: 2509-5706
Series E-ISSN: 2509-5714
Edition Number: 1
Number of Pages: XIX, 103
Number of Illustrations: 8 b/w illustrations, 2 illustrations in colour
Topics: Neurosciences, Natural Language Processing (NLP), Computational Linguistics, Data Mining and Knowledge Discovery, Information Systems Applications (incl. Internet), Computer Appl. in Social and Behavioral Sciences