000			02744nam a2200229Ia 4500
003			NULRC
005			20250520102819.0
008			250520s9999 xx 000 0 und d
020			_a9783319253411
040			_cNULRC
050			_aTA 1634 .P76 2016
245		0	_aProminent feature extraction for sentiment analysis / _cedited by Basant Agarwal and Namita Mittal
260			_aSwitzerland : _bSpringer International Publishing, _cc2016
300			_axix, 103 pages : _billustrations ; _c24 cm.
365			_bUSD90.45
504			_aIncludes bibliographical references.
505			_aIntroduction -- 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			_a 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			_aNEUROSCIENCES
700			_aBasant, Agarwal;Mittal, Namita _eeditor;co-editor
942			_2lcc _cBK
999			_c15991 _d15991