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Please use this identifier to cite or link to this item: https://ir.iimcal.ac.in:8443/jspui/handle/123456789/999
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dc.contributor.authorVallurupalli, Vamsi
dc.contributor.authorBose, Indranil
dc.date.accessioned2021-08-26T06:03:21Z-
dc.date.available2021-08-26T06:03:21Z-
dc.date.issued2020
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85078191212&doi=10.1007%2fs12525-020-00397-5&partnerID=40&md5=55fc8d561eafe60fe85bd2d364e45a54
dc.identifier.urihttps://ir.iimcal.ac.in:8443/jspui/handle/123456789/999-
dc.descriptionVamsi Vallurupalli, Indian Institute of Management Calcutta, Diamond Harbour Road, Kolkata, 700104, India; Indranil Bose, Management Information Systems, Indian Institute of Management Calcutta, Diamond Harbour Road, Kolkata, 700104, India
dc.descriptionISSN/ISBN - 10196781
dc.descriptionpp.791-804
dc.descriptionDOI - 10.1007/s12525-020-00397-5
dc.description.abstractOnline reviews are a critical component of the retail business ecosystem today. They help consumers share feedback and readers make informed choices. As such, it is important to understand the mechanism driving the creation of reviews and identify factors which make them useful for readers. Extant work in this field has largely ignored the distribution of thematic content in reviews and its role in review diagnosticity. This article attempts to bridge the gap. A novel approach is proposed to explore the distribution of thematic content in reviews, in terms of underlying topics, and test its impact on influence of reviews. The approach is illustrated through a case study using data from Yelp. Implications of the study for theory and practice are discussed. � 2020, Institute of Applied Informatics at University of Leipzig.
dc.publisherSCOPUS
dc.publisherElectronic Markets
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofseries30(4)
dc.subjectLatent Dirichlet allocation
dc.subjectOnline reviews
dc.subjectReview influence
dc.subjectThematic content
dc.subjectTopic modeling
dc.subjectYelp
dc.titleExploring thematic composition of online reviews: A topic modeling approach
dc.typeArticle
Appears in Collections:Management Information Systems

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