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Taxonomy of Online Opinion Mining Research

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dc.contributor.author Bhuiyan, Touhid
dc.date.accessioned 2018-09-06T05:12:31Z
dc.date.accessioned 2019-05-27T09:57:04Z
dc.date.available 2018-09-06T05:12:31Z
dc.date.available 2019-05-27T09:57:04Z
dc.date.issued 2009
dc.identifier.uri http://hdl.handle.net/20.500.11948/3082
dc.description.abstract Collaborative 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.iso en en_US
dc.publisher International Transactions on Computer Science and Engineering en_US
dc.subject Online Opinion Mining en_US
dc.subject Research en_US
dc.subject online customer feedback en_US
dc.subject customer reviews en_US
dc.subject Opinion Mining en_US
dc.title Taxonomy of Online Opinion Mining Research en_US
dc.type Article en_US


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