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rdf:type
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rdfs:seeAlso
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Description
| - 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.
- 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)
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Title
| - Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers
- Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers (en)
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skos:prefLabel
| - Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers
- Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers (en)
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skos:notation
| - RIV/62156489:43110/13:00199773!RIV14-MSM-43110___
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/62156489:43110/13:00199773
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - customer reviews; text mining; e-shopping; Internet; business intelligence; machine learning; automatic categorization; natural language processing (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...i/riv/mistoVydani
| - Hershey, Pennsylvania (USA)
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http://linked.open...vEdiceCisloSvazku
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http://linked.open...i/riv/nazevZdroje
| - Transdisciplinary Marketing Concepts and Emergent Methods for Virtual Environments
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...v/pocetStranKnihy
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http://linked.open...cetTvurcuVysledku
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http://linked.open...UplatneniVysledku
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http://linked.open...iv/tvurceVysledku
| - Žižka, Jan
- Rukavitsyn, Vadim
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http://linked.open...n/vavai/riv/zamer
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number of pages
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http://purl.org/ne...btex#hasPublisher
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https://schema.org/isbn
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http://localhost/t...ganizacniJednotka
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