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Statements

Subject Item
n2:RIV%2F62156489%3A43110%2F13%3A00199773%21RIV14-MSM-43110___
rdf:type
n16:Vysledek skos:Concept
rdfs:seeAlso
http://www.igi-global.com/chapter/automatic-categorization-reviews-opinions-internet/67517
dcterms: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.
dcterms:title
Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers
skos:prefLabel
Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers Automatic Categorization of Reviews and Opinions of Internet E-Shopping Customers
skos:notation
RIV/62156489:43110/13:00199773!RIV14-MSM-43110___
n16:predkladatel
n21:orjk%3A43110
n3:aktivita
n11:Z
n3:aktivity
Z(MSM6215648904)
n3:dodaniDat
n12:2014
n3:domaciTvurceVysledku
n14:9299483
n3:druhVysledku
n22:C
n3:duvernostUdaju
n9:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
62586
n3:idVysledku
RIV/62156489:43110/13:00199773
n3:jazykVysledku
n19:eng
n3:klicovaSlova
customer reviews; text mining; e-shopping; Internet; business intelligence; machine learning; automatic categorization; natural language processing
n3:klicoveSlovo
n6:text%20mining n6:e-shopping n6:Internet n6:natural%20language%20processing n6:customer%20reviews n6:machine%20learning n6:business%20intelligence n6:automatic%20categorization
n3:kontrolniKodProRIV
[EF8397AC8C52]
n3:mistoVydani
Hershey, Pennsylvania (USA)
n3:nazevEdiceCisloSvazku
1
n3:nazevZdroje
Transdisciplinary Marketing Concepts and Emergent Methods for Virtual Environments
n3:obor
n10:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetStranKnihy
385
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n12:2013
n3:tvurceVysledku
Rukavitsyn, Vadim Žižka, Jan
n3:zamer
n15:MSM6215648904
s:numberOfPages
10
n8:hasPublisher
IGI Global
n13:isbn
978-1-4666-1861-9
n17:organizacniJednotka
43110