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dc.contributor.authorBhuiyan, Touhid
dc.date.accessioned2018-09-06T05:12:31Z
dc.date.available2018-09-06T05:12:31Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/20.500.11948/3082
dc.description.abstractCollaborative Filtering is the most popular technique for Recommender Systems which collects opinions from customers in the form of ratings on items, services or service providers. In addition to the customer rating, there is also a good number of online customer feedback information available over the Internet as customer reviews, comments, newsgroups post, discussion forums or blogs which is collectively called user generated contents. This information can be used to generate the public reputation of the service provider. To do this, data mining techniques, specially recently emerged opinion mining could be a useful tool. In this paper we present a review of Opinion Mining from online customer feedback. We critically evaluate the existing work and expose cutting edge area of interest in opinion mining. We also classify the approaches taken by different researchers into several categories and subcategories. Each of those steps is analyzed with their strength and limitations in this paper.en_US
dc.language.isoenen_US
dc.publisherInternational Transactions on Computer Science and Engineeringen_US
dc.subjectOnline Opinion Miningen_US
dc.subjectResearchen_US
dc.subjectonline customer feedbacken_US
dc.subjectcustomer reviewsen_US
dc.subjectOpinion Miningen_US
dc.titleTaxonomy of Online Opinion Mining Researchen_US
dc.typeArticleen_US


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