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Title: Sentiment analysis in online reviews classification using text mining technique
Authors: Moreno, A.
Rita, P.
Guerreiro, J.
Editors: A. Rocha, I. Pedrosa, M. P. Cota, R. Gonçalves
Keywords: Text mining
Online reviews
Sentiments analysis
Natural language processing
Topic models
Issue Date: 2019
Publisher: IEEE
Abstract: The growth of social media in recent years has led to an increase in online reviews that reflects consumer opinions. Firms benefit greatly from making this information available in order to respond more effectively to consumer dissatisfaction and to exploit market opportunities by observing standards that may represent unsatisfied needs. The present study aims to address this problem through a survey based on the Yelp platform. To this end, 14,000 comments related to different tourism products were used and text mining techniques and topic models were applied to find the main latent topics discussed in the online comments and their associated sentiments. The study presents 20 latent topics from online discussions and reveals that the topic that discusses “Air Travel” themes is the one with a lower sentiment connotation on average and should therefore be the subject of a deeper evaluation.
Peer reviewed: yes
DOI: 10.23919/CISTI.2019.8760671
ISBN: 978-989-98434-9-3
ISSN: 2166-0727
Accession number: WOS:000492038200082
Appears in Collections:ISTAR-CRI - Comunicações a conferências internacionais
BRU-CRI - Comunicações a conferências internacionais

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