. "Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers" . . "2"^^ . . "E-shopping customers, blog authors, reviewers, and other web contributors can express their opinions of a purchased item, film, book, and so forth. Typically, various opinions are centered around one topic (e.g., a commodity, film, etc.). From the Business Intelligence viewpoint, such entries are very valuable; however, they are difficult to automatically process because they are in a natural language. Human beings can distinguish the various opinions. Because of the very large data volumes, could a machine do the same? The suggested method uses the machine-learning (ML) based approach to this classification problem, demonstrating via real-world data that a machine can learn from examples relatively well. The classification accuracy is better than 70%; it is not perfect because of typical problems associated with processing unstructured textual items in natural languages. The data characteristics and experimental results are shown." . "IGI Global" . "1"^^ . . . . "Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers"@en . . . "978-1-4666-1861-9" . . "Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers" . "1" . "RIV/62156489:43110/13:00199773!RIV14-MSM-43110___" . "Hershey, Pennsylvania (USA)" . . . "RIV/62156489:43110/13:00199773" . "62586" . "Rukavitsyn, Vadim" . "Transdisciplinary Marketing Concepts and Emergent Methods for Virtual Environments" . "43110" . . . "10"^^ . . "[EF8397AC8C52]" . . "http://www.igi-global.com/chapter/automatic-categorization-reviews-opinions-internet/67517" . "385"^^ . "customer reviews; text mining; e-shopping; Internet; business intelligence; machine learning; automatic categorization; natural language processing"@en . . . . "\u017Di\u017Eka, Jan" . . . "E-shopping customers, blog authors, reviewers, and other web contributors can express their opinions of a purchased item, film, book, and so forth. Typically, various opinions are centered around one topic (e.g., a commodity, film, etc.). From the Business Intelligence viewpoint, such entries are very valuable; however, they are difficult to automatically process because they are in a natural language. Human beings can distinguish the various opinions. Because of the very large data volumes, could a machine do the same? The suggested method uses the machine-learning (ML) based approach to this classification problem, demonstrating via real-world data that a machine can learn from examples relatively well. The classification accuracy is better than 70%; it is not perfect because of typical problems associated with processing unstructured textual items in natural languages. The data characteristics and experimental results are shown."@en . "Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers"@en . . "Z(MSM6215648904)" .