Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/18474
Author(s): | Guerreiro, J. Rita, P. |
Date: | 2020 |
Title: | How to predict explicit recommendations in online reviews using text mining and sentiment analysis |
Volume: | 43 |
Pages: | 269 - 272 |
ISSN: | 1447-6770 |
DOI (Digital Object Identifier): | 10.1016/j.jhtm.2019.07.001 |
Keywords: | eWOM Online recommendations Text mining CART |
Abstract: | Opinions shared by peer travelers help tourists decrease the risks of making a poor decision. However, the increasing number of reviews per experience makes it difficult to read all reviews for an informed decision. Therefore, reviewers who make a personal and explicit recommendation of the services by using expressions such as “I highly recommend” or “don't recommend” may help consumers in their decision-making process. Such reviews suggest that the reviewer was satisfied to a point that (s)he would advise others to try or was unsatisfied and will for sure avoid coming back. The current research note explores what may drive reviewers to make direct endorsements in text. A text mining method was applied to online reviews to identify drivers of explicit recommendations. Lack of competences from the provider and negative attitudes are triggers of negative direct recommendations, whereas positive feelings predict a positive recommendation in the body of the review. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
JTHM_RN_Main Document_R2.pdf | Pós-print | 529,35 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.