For the rapidly increasing amount of information available on the Internet, little quality control exists, especially over the user-generated content. Manually scanning through large amounts of user-generated content is time-consuming and sometime impossible. In this case, opinion mining is a better alternative. Although it is recognized that the opinion reviews contain valuable information for a variety of applications, the lack of quality control attracts spammers who have found many ways to draw their benefits from spamming. Moreover, the spam detection problem is complex because spammers always invent fresh methods that can't be easily recognized. The work of this book presents a new approach for performing spam detection in Arabic opinion reviews by merging methods from data mining and text mining in one mining classification approach. Classification algorithms are applied on TBA dataset, ATBA corpus, and ATBAH dataset. The experimental results show that the proposed approach is effective in identifying Arabic spam opinion reviews. Our designed machine learning achieves significant improvements. In the best case, our F-measure is improved up to 99.59%.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
For the rapidly increasing amount of information available on the Internet, little quality control exists, especially over the user-generated content. Manually scanning through large amounts of user-generated content is time-consuming and sometime impossible. In this case, opinion mining is a better alternative. Although it is recognized that the opinion reviews contain valuable information for a variety of applications, the lack of quality control attracts spammers who have found many ways to draw their benefits from spamming. Moreover, the spam detection problem is complex because spammers always invent fresh methods that can't be easily recognized. The work of this book presents a new approach for performing spam detection in Arabic opinion reviews by merging methods from data mining and text mining in one mining classification approach. Classification algorithms are applied on TBA dataset, ATBA corpus, and ATBAH dataset. The experimental results show that the proposed approach is effective in identifying Arabic spam opinion reviews. Our designed machine learning achieves significant improvements. In the best case, our F-measure is improved up to 99.59%.
Programmer, The College of Science and Technology, Khan Younis - Palestine, BSc & M.Sc. in Information Technology from The Islamic University of Gaza - Palestine. Research Interest: Data Mining, Text Mining, Arabic Natural Language Processing and Understanding, Software Development, and Web Computing.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -For the rapidly increasing amount of information available on the Internet, little quality control exists, especially over the user-generated content. Manually scanning through large amounts of user-generated content is time-consuming and sometime impossible. In this case, opinion mining is a better alternative. Although it is recognized that the opinion reviews contain valuable information for a variety of applications, the lack of quality control attracts spammers who have found many ways to draw their benefits from spamming. Moreover, the spam detection problem is complex because spammers always invent fresh methods that can't be easily recognized. The work of this book presents a new approach for performing spam detection in Arabic opinion reviews by merging methods from data mining and text mining in one mining classification approach. Classification algorithms are applied on TBA dataset, ATBA corpus, and ATBAH dataset. The experimental results show that the proposed approach is effective in identifying Arabic spam opinion reviews. Our designed machine learning achieves significant improvements. In the best case, our F-measure is improved up to 99.59%. 116 pp. Englisch. N° de réf. du vendeur 9783659382727
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Vendeur : moluna, Greven, Allemagne
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Abu Hammad Ahmed S.Programmer, The College of Science and Technology, Khan Younis - Palestine, BSc & M.Sc. in Information Technology from The Islamic University of Gaza - Palestine. Research Interest: Data Mining, Text Mining, Arabic. N° de réf. du vendeur 5152571
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - For the rapidly increasing amount of information available on the Internet, little quality control exists, especially over the user-generated content. Manually scanning through large amounts of user-generated content is time-consuming and sometime impossible. In this case, opinion mining is a better alternative. Although it is recognized that the opinion reviews contain valuable information for a variety of applications, the lack of quality control attracts spammers who have found many ways to draw their benefits from spamming. Moreover, the spam detection problem is complex because spammers always invent fresh methods that can't be easily recognized. The work of this book presents a new approach for performing spam detection in Arabic opinion reviews by merging methods from data mining and text mining in one mining classification approach. Classification algorithms are applied on TBA dataset, ATBA corpus, and ATBAH dataset. The experimental results show that the proposed approach is effective in identifying Arabic spam opinion reviews. Our designed machine learning achieves significant improvements. In the best case, our F-measure is improved up to 99.59%. N° de réf. du vendeur 9783659382727
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Vendeur : preigu, Osnabrück, Allemagne
Taschenbuch. Etat : Neu. Opinion Spam Detection | An Approach for Detecting Spam in Arabic Opinion Reviews | Ahmed S. Abu Hammad | Taschenbuch | 116 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659382727 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. N° de réf. du vendeur 105956862
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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -For the rapidly increasing amount of information available on the Internet, little quality control exists, especially over the user-generated content. Manually scanning through large amounts of user-generated content is time-consuming and sometime impossible. In this case, opinion mining is a better alternative. Although it is recognized that the opinion reviews contain valuable information for a variety of applications, the lack of quality control attracts spammers who have found many ways to draw their benefits from spamming. Moreover, the spam detection problem is complex because spammers always invent fresh methods that can't be easily recognized. The work of this book presents a new approach for performing spam detection in Arabic opinion reviews by merging methods from data mining and text mining in one mining classification approach. Classification algorithms are applied on TBA dataset, ATBA corpus, and ATBAH dataset. The experimental results show that the proposed approach is effective in identifying Arabic spam opinion reviews. Our designed machine learning achieves significant improvements. In the best case, our F-measure is improved up to 99.59%.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 116 pp. Englisch. N° de réf. du vendeur 9783659382727
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Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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