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Title: Sentiment classification of consumer generated online reviews using topic modeling
Authors: Calheiros, A. C.
Moro, S.
Rita, P.
Keywords: Customer reviews
Text mining
Topic modeling
Sentiment classification
Issue Date: 2017
Publisher: Taylor and Francis
Abstract: The development of the Internet and mobile devices enabled the emergence of travel and hospitality review sites, leading to a large number of customer opinion posts. While such comments may influence future demand of the targeted hotels, they can also be used by hotel managers to improve customer experience. In this article, sentiment classification of an eco-hotel is assessed through a text mining approach using several different sources of customer reviews. The latent Dirichlet allocation modeling algorithm is applied to gather relevant topics that characterize a given hospitality issue by a sentiment. Several findings were unveiled including that hotel food generates ordinary positive sentiments, while hospitality generates both ordinary and strong positive feelings. Such results are valuable for hospitality management, validating the proposed approach.
Peer reviewed: yes
DOI: 10.1080/19368623.2017.1310075
ISSN: 1936-8623
Accession number: WOS:000410908900001
Appears in Collections:ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica
BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica

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