Inhaltsangabe
Opinion mining is used to improve the decision making of new user in various domains such as product, movie, news media, social networking shares etc. Feature based opinion mining rely only on single domain corpus in most of the existing methodology. Feature based opinion mining in two different domain corpuses is complex. The features and Opinion words are extracted with the help of the Part-of-Speech (PoS) tagging tool. The Inter dependent domain relevance (IDDR) technique use removal of redundant features and pruning of irrelevant features from two different domains with the help of the IDDR score and threshold value. Normally data mining and machine learning use training and test data from same domain and have same feature. But the above concept is not hold in all domains due to the lack of labeled dataset. Here the proposed transfer learning method using Exaggerate Instance weighted K nearest neighbor (EIWKNN) algorithm to transfer the knowledge from camera domain to iPod domain for Opinion classification. The summary of two different domains feature with respect to their opinion is generated.
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Über die Autorin bzw. den Autor
Dr. S. Ramesh, Faculty in the Department of Computer Science and Engineering at Anna University Regional Centre, Madurai. Received his Ph.D in Information and Communication Engineering by 2015 from Anna University, Chennai. His research interests include Wireless Communication, Network Security and Optimization Techniques.
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Feature Based Opinion Summarization using Transfer Learning
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Feature Based Opinion Summarization using Transfer Learning
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Opinion mining is used to improve the decision making of new user in various domains such as product, movie, news media, social networking shares etc. Feature based opinion mining rely only on single domain corpus in most of the existing methodology. Feature based opinion mining in two different domain corpuses is complex. The features and Opinion words are extracted with the help of the Part-of-Speech (PoS) tagging tool. The Inter dependent domain relevance (IDDR) technique use removal of redundant features and pruning of irrelevant features from two different domains with the help of the IDDR score and threshold value. Normally data mining and machine learning use training and test data from same domain and have same feature. But the above concept is not hold in all domains due to the lack of labeled dataset. Here the proposed transfer learning method using Exaggerate Instance weighted K nearest neighbor (EIWKNN) algorithm to transfer the knowledge from camera domain to iPod domain for Opinion classification. The summary of two different domains feature with respect to their opinion is generated. 88 pp. Englisch. Bestandsnummer des Verkäufers 9783659717949
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sekaran RameshDr. S. Ramesh, Faculty in the Department of Computer Science and Engineering at Anna University Regional Centre, Madurai. Received his Ph.D in Information and Communication Engineering by 2015 from Anna University, Chen. Bestandsnummer des Verkäufers 158428983
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Feature Based Opinion Summarization using Transfer Learning
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Opinion mining is used to improve the decision making of new user in various domains such as product, movie, news media, social networking shares etc. Feature based opinion mining rely only on single domain corpus in most of the existing methodology. Feature based opinion mining in two different domain corpuses is complex. The features and Opinion words are extracted with the help of the Part-of-Speech (PoS) tagging tool. The Inter dependent domain relevance (IDDR) technique use removal of redundant features and pruning of irrelevant features from two different domains with the help of the IDDR score and threshold value. Normally data mining and machine learning use training and test data from same domain and have same feature. But the above concept is not hold in all domains due to the lack of labeled dataset. Here the proposed transfer learning method using Exaggerate Instance weighted K nearest neighbor (EIWKNN) algorithm to transfer the knowledge from camera domain to iPod domain for Opinion classification. The summary of two different domains feature with respect to their opinion is generated.Books on Demand GmbH, Überseering 33, 22297 Hamburg 88 pp. Englisch. Bestandsnummer des Verkäufers 9783659717949
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Feature Based Opinion Summarization using Transfer Learning : Research Perspective
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Opinion mining is used to improve the decision making of new user in various domains such as product, movie, news media, social networking shares etc. Feature based opinion mining rely only on single domain corpus in most of the existing methodology. Feature based opinion mining in two different domain corpuses is complex. The features and Opinion words are extracted with the help of the Part-of-Speech (PoS) tagging tool. The Inter dependent domain relevance (IDDR) technique use removal of redundant features and pruning of irrelevant features from two different domains with the help of the IDDR score and threshold value. Normally data mining and machine learning use training and test data from same domain and have same feature. But the above concept is not hold in all domains due to the lack of labeled dataset. Here the proposed transfer learning method using Exaggerate Instance weighted K nearest neighbor (EIWKNN) algorithm to transfer the knowledge from camera domain to iPod domain for Opinion classification. The summary of two different domains feature with respect to their opinion is generated. Bestandsnummer des Verkäufers 9783659717949
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Feature Based Opinion Summarization using Transfer Learning | Research Perspective
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Taschenbuch. Zustand: Neu. Feature Based Opinion Summarization using Transfer Learning | Research Perspective | Ramesh Sekaran (u. a.) | Taschenbuch | 88 S. | Englisch | 2015 | LAP Lambert Academic Publishing | EAN 9783659717949 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 104564203
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