An Empirical Examination of Online Restaurant Reviews

Hyun Jung Kang, Iris Eshkol-Taravella

Abstract
In the wake of (Pang et al., 2002; Turney, 2002; Liu, 2012) inter alia, opinion mining and sentiment analysis have focused on extracting either positive or negative opinions from texts and determining the targets of these opinions. In this study, we go beyond the coarse-grained positive vs. negative opposition and propose a corpus-based scheme that detects evaluative language at a finer-grained level. We classify each sentence into one of four evaluation types based on the proposed scheme: (1) the reviewer’s opinion on the restaurant (positive, negative, or mixed); (2) the reviewer’s input/feedback to potential customers and restaurant owners (suggestion, advice, or warning) (3) whether the reviewer wants to return to the restaurant (intention); (4) the factual statement about the experience (description). We apply classical machine learning and deep learning methods to show the effectiveness of our scheme. We also interpret the performances that we obtained for each category by taking into account the specificities of the corpus treated.
Anthology ID:
2020.lrec-1.608
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4942–4947
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.608
DOI:
Bibkey:
Cite (ACL):
Hyun Jung Kang and Iris Eshkol-Taravella. 2020. An Empirical Examination of Online Restaurant Reviews. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 4942–4947, Marseille, France. European Language Resources Association.
Cite (Informal):
An Empirical Examination of Online Restaurant Reviews (Kang & Eshkol-Taravella, LREC 2020)
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PDF:
https://preview.aclanthology.org/naacl24-info/2020.lrec-1.608.pdf
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