MARC details
| 000 -LEADER |
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| fixed length control field |
02744nam a2200229Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
NULRC |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250520102819.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250520s9999 xx 000 0 und d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783319253411 |
| 040 ## - CATALOGING SOURCE |
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| Transcribing agency |
NULRC |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
TA 1634 .P76 2016 |
| 245 #0 - TITLE STATEMENT |
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| Title |
Prominent feature extraction for sentiment analysis / |
| Statement of responsibility, etc. |
edited by Basant Agarwal and Namita Mittal |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Switzerland : |
| Name of publisher, distributor, etc. |
Springer International Publishing, |
| Date of publication, distribution, etc. |
c2016 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xix, 103 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
24 cm. |
| 365 ## - TRADE PRICE |
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| Price amount |
USD90.45 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references. |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Introduction -- Literature Survey -- Machine Learning Approach for Sentiment Analysis -- Semantic Parsing using Dependency Rules -- Sentiment Analysis using ConceptNet Ontology and Context Information -- Semantic Orientation based Approach for Sentiment Analysis -- Conclusions and FutureWork -- References -- Glossary -- Index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
NEUROSCIENCES |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Basant, Agarwal;Mittal, Namita |
| Relator term |
editor;co-editor |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Books |
