Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/13343
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dc.contributor.authorKrishnamoorthy, Srikumar
dc.date.accessioned2015-04-23T06:00:53Z
dc.date.available2015-04-23T06:00:53Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/11718/13343
dc.description.abstractHelpfulness of online reviews plays an important role in customer purchase decision making process. However, the review helpfulness prediction problem is considered to be quite challenging and hard. This paper aims to explore this problem and build a helpfulness pre- diction model. Our model utilizes a rich set of features based on textual content of reviews, meta-data of reviews and characteristics of reviewers. The proposed predictive model is validated using six real-life review datasets and the experimental results are found to be quite promising. Our experimental analysis of the impact of product type such as search and experience goods on review helpfulness also reveals interesting insights.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Management , Ahmedabaden_US
dc.relation.ispartofseriesWP;2371
dc.subjectOnline Reviewsen_US
dc.subjectCustomer Reviewen_US
dc.titlePredicting helpfulness of online customer reviewsen_US
dc.typeWorking Paperen_US
Appears in Collections:Working Papers

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