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Prominent feature extraction for sentiment analysis [electronic resource]

Prominent feature extraction for sentiment analysis [electronic resource]

자료유형
E-Book(소장)
개인저자
Agarwal, Basant. Mittal, Namita.
서명 / 저자사항
Prominent feature extraction for sentiment analysis [electronic resource] / by Basant Agarwal, Namita Mittal.
발행사항
Cham :   Springer International Publishing :   Imprint: Springer,   c2016.  
형태사항
1 online resource (xix, 103 p.) : ill. (some col.).
총서사항
Socio-affective computing
ISBN
9783319253435
요약
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.
일반주기
Title from e-Book title page.  
내용주기
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.
서지주기
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Medicine. Semantic computing. Data mining. Neurosciences. Text processing (Computer science). Computational linguistics.
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020 ▼a 9783319253435
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100 1 ▼a Agarwal, Basant.
245 1 0 ▼a Prominent feature extraction for sentiment analysis ▼h [electronic resource] / ▼c by Basant Agarwal, Namita Mittal.
260 ▼a Cham : ▼b Springer International Publishing : ▼b Imprint: Springer, ▼c c2016.
300 ▼a 1 online resource (xix, 103 p.) : ▼b ill. (some col.).
490 1 ▼a Socio-affective computing
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
505 0 ▼a 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 ▼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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Medicine.
650 0 ▼a Semantic computing.
650 0 ▼a Data mining.
650 0 ▼a Neurosciences.
650 0 ▼a Text processing (Computer science).
650 0 ▼a Computational linguistics.
700 1 ▼a Mittal, Namita.
830 0 ▼a Socio-affective computing.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-319-25343-5
945 ▼a KLPA
991 ▼a E-Book(소장)

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