Editore: Springer International Publishing AG, Cham, 2019
ISBN 10: 3319797751 ISBN 13: 9783319797755
Lingua: Inglese
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
EUR 106,52
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. 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 thetext 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Editore: Springer International Publishing AG, Cham, 2015
ISBN 10: 3319253417 ISBN 13: 9783319253411
Lingua: Inglese
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
EUR 106,52
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: new. Hardcover. 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 thetext 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 102,99
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 102,99
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Da: California Books, Miami, FL, U.S.A.
EUR 117,13
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 110,03
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Da: California Books, Miami, FL, U.S.A.
EUR 127,78
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 115,65
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Da: Books Puddle, New York, NY, U.S.A.
EUR 136,92
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Aggiungi al carrelloCondizione: New. pp. 122.
Editore: Springer International Publishing, 2019
ISBN 10: 3319797751 ISBN 13: 9783319797755
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 92,27
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Editore: Springer International Publishing, 2015
ISBN 10: 3319253417 ISBN 13: 9783319253411
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 92,27
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Aggiungi al carrelloCondizione: New.
EUR 143,35
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Aggiungi al carrelloCondizione: New. pp.
EUR 149,54
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Aggiungi al carrelloPaperback. Condizione: Brand New. reprint edition. 124 pages. 9.25x6.10x0.28 inches. In Stock.
EUR 151,44
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Aggiungi al carrelloHardcover. Condizione: Brand New. 9.25x6.25x0.50 inches. In Stock.
Editore: Springer International Publishing, 2015
ISBN 10: 3319253417 ISBN 13: 9783319253411
Lingua: Inglese
Da: Buchpark, Trebbin, Germania
EUR 79,25
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Aggiungi al carrelloCondizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 162,31
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Aggiungi al carrelloPaperback. Condizione: New. New. book.
Editore: Springer International Publishing AG, Cham, 2019
ISBN 10: 3319797751 ISBN 13: 9783319797755
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 188,16
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: new. Paperback. 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 thetext 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Editore: Springer International Publishing AG, Cham, 2015
ISBN 10: 3319253417 ISBN 13: 9783319253411
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 188,16
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: new. Hardcover. 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 thetext 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Da: Majestic Books, Hounslow, Regno Unito
EUR 143,75
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 122.
Da: Majestic Books, Hounslow, Regno Unito
EUR 150,96
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 145,90
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 122.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 152,49
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp.