A Hybrid Approach of Opinion Mining and Comparative Linguistic Analysis of Restaurant Reviews

Salim Sazzed


Abstract
The existing research on sentiment analysis mainly utilized data curated in limited geographical regions and demography (e.g., USA, UK, China) due to commercial interest and availability of review data. Since the user’s attitudes and preferences can be affected by numerous sociocultural factors and demographic characteristics, it is necessary to have annotated review datasets belong to various demography. In this work, we first construct a review dataset BanglaRestaurant that contains over 2300 customer reviews towards a number of Bangladeshi restaurants. Then, we present a hybrid methodology that yields improvement over the best performing lexicon-based and machine learning (ML) based classifier without using any labeled data. Finally, we investigate how the demography (i.e., geography and nativeness in English) of users affect the linguistic characteristics of the reviews by contrasting two datasets, BanglaRestaurant and Yelp. The comparative results demonstrate the efficacy of the proposed hybrid approach. The data analysis reveals that demography plays an influential role in the linguistic aspects of reviews.
Anthology ID:
2021.ranlp-1.144
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1281–1288
Language:
URL:
https://aclanthology.org/2021.ranlp-1.144
DOI:
Bibkey:
Cite (ACL):
Salim Sazzed. 2021. A Hybrid Approach of Opinion Mining and Comparative Linguistic Analysis of Restaurant Reviews. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1281–1288, Held Online. INCOMA Ltd..
Cite (Informal):
A Hybrid Approach of Opinion Mining and Comparative Linguistic Analysis of Restaurant Reviews (Sazzed, RANLP 2021)
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PDF:
https://aclanthology.org/2021.ranlp-1.144.pdf