Opinion Spam Detection: An Approach for Detecting Spam in Arabic Opinion Reviews

 
9783659382727: Opinion Spam Detection: An Approach for Detecting Spam in Arabic Opinion Reviews

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%.

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About the Author:

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.

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Book Description Book Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | An Approach for Detecting Spam in Arabic Opinion Reviews | 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%. | Format: Paperback | Language/Sprache: english | 116 pp. Bookseller Inventory # K9783659382727

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Book Description LAP Lambert Academic Publishing Apr 2013, 2013. Taschenbuch. Book Condition: Neu. 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. Bookseller Inventory # 9783659382727

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Book Description LAP Lambert Academic Publishing Apr 2013, 2013. Taschenbuch. Book Condition: Neu. 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. Bookseller Inventory # 9783659382727

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Book Description LAP Lambert Academic Publishing, United States, 2013. Paperback. Book Condition: New. Language: English . Brand New Book. 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 . Bookseller Inventory # KNV9783659382727

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Book Description LAP Lambert Academic Publishing Apr 2013, 2013. Taschenbuch. Book Condition: Neu. This item is printed on demand - Print on Demand 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. Bookseller Inventory # 9783659382727

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Book Description LAP Lambert Academic Publishing. Paperback. Book Condition: New. Paperback. 116 pages. Dimensions: 8.7in. x 5.9in. x 0.3in.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 cant 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. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Bookseller Inventory # 9783659382727

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