9783319797755 - prominent feature extraction for sentiment analysis: 2 di agarwal, basant; mittal, namita (13 risultati)
Lingua: Inglese
Editore: Springer, 2019
Serie: Socio-Affective Computing, Libro 2 di 10. Libro 2 di 10 - Socio-Affective Computing
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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EUR 115,63
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Condizione: New. In.
Lingua: Inglese
Editore: Springer, 2019
Serie: Socio-Affective Computing, Libro 2 di 10. Libro 2 di 10 - Socio-Affective Computing
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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EUR 140,50
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Condizione: New. pp. 122.
Lingua: Inglese
Editore: Springer International Publishing, 2019
Serie: Socio-Affective Computing, Libro 2 di 10. Libro 2 di 10 - Socio-Affective Computing
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Da: moluna, Greven, Germaniamoluna
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EUR 92,27
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Condizione: New.
Lingua: Inglese
Editore: Springer Verlag, 2017
Serie: Socio-Affective Computing, Libro 2 di 10. Libro 2 di 10 - Socio-Affective Computing
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 151,63
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Paperback. Condizione: Brand New. reprint edition. 124 pages. 9.25x6.10x0.28 inches. In Stock.
Altre immaginiLingua: Inglese
Editore: Springer, 2019
Serie: Socio-Affective Computing, Libro 2 di 10. Libro 2 di 10 - Socio-Affective Computing
- Brossura
Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Prominent Feature Extraction for Sentiment Analysis | Basant Agarwal (u. a.) | Taschenbuch | Socio-Affective Computing | xix | Englisch | 2019 | Springer | EAN 9783319797755 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]spring…er[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing, 2019
Serie: Socio-Affective Computing, Libro 2 di 10. Libro 2 di 10 - Socio-Affective Computing
- Brossura
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 106,99
EUR 61,01 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. 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… 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, 2019
Serie: Socio-Affective Computing, Libro 2 di 10. Libro 2 di 10 - Socio-Affective Computing
- Brossura
Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
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EUR 167,24
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Paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Lingua: Inglese
Editore: Springer, 2019
Serie: Socio-Affective Computing, Libro 2 di 10. Libro 2 di 10 - Socio-Affective Computing
- Brossura
- Print on Demand
Da: Basi6 International, Irving, TX, U.S.A.Basi6 International
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EUR 92,85
Spedizione gratuitaSpedito in U.S.A.Quantità: Più di 20 disponibili
Condizione: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days.
Lingua: Inglese
Editore: Springer, 2019
Serie: Socio-Affective Computing, Libro 2 di 10. Libro 2 di 10 - Socio-Affective Computing
- Brossura
- Print on Demand
Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 86,24
EUR 5,50 spedizioneSpedito da Italia a U.S.A.Quantità: Più di 20 disponibili
Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing Mrz 2019, 2019
Serie: Socio-Affective Computing, Libro 2 di 10. Libro 2 di 10 - Socio-Affective Computing
- Brossura
- 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
Taschenbuch. 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 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. 124 pp. Englisch.
Lingua: Inglese
Editore: Springer, 2019
Serie: Socio-Affective Computing, Libro 2 di 10. Libro 2 di 10 - Socio-Affective Computing
- Brossura
- Print on Demand
Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 147,25
EUR 7,65 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 4 disponibili
Condizione: New. Print on Demand pp. 122.
Lingua: Inglese
Editore: Springer, 2019
Serie: Socio-Affective Computing, Libro 2 di 10. Libro 2 di 10 - Socio-Affective Computing
- Brossura
- Print on Demand
Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 147,74
EUR 9,95 spedizioneSpedito da Germania a U.S.A.Quantità: 4 disponibili
Condizione: New. PRINT ON DEMAND pp. 122.
Lingua: Inglese
Editore: Springer, Palgrave Macmillan Mär 2019, 2019
Serie: Socio-Affective Computing, Libro 2 di 10. Libro 2 di 10 - Socio-Affective Computing
- Brossura
- 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
Taschenbuch. 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 use…s 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 124 pp. Englisch.
