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Prominent feature extraction for sentiment analysis [electronic resource]

Prominent feature extraction for sentiment analysis [electronic resource]

Material type
E-Book(소장)
Personal Author
Agarwal, Basant. Mittal, Namita.
Title Statement
Prominent feature extraction for sentiment analysis [electronic resource] / by Basant Agarwal, Namita Mittal.
Publication, Distribution, etc
Cham :   Springer International Publishing :   Imprint: Springer,   c2016.  
Physical Medium
1 online resource (xix, 103 p.) : ill. (some col.).
Series Statement
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.
General Note
Title from e-Book title page.  
Content Notes
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.
Bibliography, Etc. Note
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
Subject Added Entry-Topical Term
Medicine. Semantic computing. Data mining. Neurosciences. Text processing (Computer science). Computational linguistics.
Short cut
URL
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007 cr
008 200414s2016 sz a ob 001 0 eng d
020 ▼a 9783319253435
040 ▼a 211009 ▼c 211009 ▼d 211009
050 4 ▼a RC321-580
082 0 4 ▼a 006 ▼2 23
084 ▼a 006 ▼2 DDCK
090 ▼a 006
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)

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/e-Book Collection/ Call Number CR 006 Accession No. E14020812 Availability Loan can not(reference room) Due Date Make a Reservation Service M

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