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 12th Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 4942–4947
- URL:
- https://www.aclweb.org/anthology/2020.lrec-1.608
- DOI:
- PDF:
- https://www.aclweb.org/anthology/2020.lrec-1.608.pdf
You can write comments here (and agree to place them under CC-by). They are not guaranteed to stay and there is no e-mail functionality.