9783319253411 - prominent feature extraction for sentiment analysis: 2 di agarwal, basant; mittal, namita (11 risultati)
Lingua: Inglese
Editore: Springer, 2015
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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EUR 116,63
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Condizione: New. In.
Lingua: Inglese
Editore: Springer International Publishing, 2015
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Da: moluna, Greven, Germaniamoluna
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EUR 92,27
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: Più di 20 disponibili
Gebunden. Condizione: New.
Lingua: Inglese
Editore: Springer, 2015
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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EUR 147,11
EUR 3,42 spedizioneSpedito in U.S.A.Quantità: 4 disponibili
Condizione: New. pp.
Lingua: Inglese
Editore: Springer, 2015
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 151,05
EUR 11,69 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 2 disponibili
Hardcover. Condizione: Brand New. 9.25x6.25x0.50 inches. In Stock.
Lingua: Inglese
Editore: Springer International Publishing, 2015
- Rilegato
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 106,99
EUR 61,80 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 relati…ons 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.
Lingua: Inglese
Editore: Palgrave Macmillan, 2015
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Da: Buchpark, Trebbin, GermaniaBuchpark
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EUR 80,25
EUR 105,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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.
Lingua: Inglese
Editore: Springer, 2015
- Rilegato
- Print on Demand
Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 86,24
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing Dez 2015, 2015
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- Print on Demand
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 106,99
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 d…ependency 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. 124 pp. Englisch.
Lingua: Inglese
Editore: Springer, 2015
- Rilegato
- Print on Demand
Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 151,59
EUR 7,60 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 4 disponibili
Condizione: New. Print on Demand pp.
Lingua: Inglese
Editore: Springer, 2015
- Rilegato
- Print on Demand
Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 155,41
EUR 9,95 spedizioneSpedito da Germania a U.S.A.Quantità: 4 disponibili
Condizione: New. PRINT ON DEMAND pp.
Lingua: Inglese
Editore: Springer, Palgrave Macmillan Dez 2015, 2015
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- Print on Demand
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 106,99
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 depen…dency 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 124 pp. Englisch.
