TY - GEN
T1 - A Hybrid Approach of Opinion Mining and Comparative Linguistic Analysis of Restaurant Reviews
AU - Sazzed, Salim
N1 - Publisher Copyright:
© 2021 Incoma Ltd. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85123631970
U2 - 10.26615/978-954-452-072-4_144
DO - 10.26615/978-954-452-072-4_144
M3 - Conference article
AN - SCOPUS:85123631970
T3 - International Conference Recent Advances in Natural Language Processing, RANLP
SP - 1281
EP - 1288
BT - International Conference Recent Advances in Natural Language Processing, RANLP 2021
A2 - Angelova, Galia
A2 - Kunilovskaya, Maria
A2 - Mitkov, Ruslan
A2 - Nikolova-Koleva, Ivelina
PB - Incoma Ltd
T2 - International Conference on Recent Advances in Natural Language Processing: Deep Learning for Natural Language Processing Methods and Applications, RANLP 2021
Y2 - 1 September 2021 through 3 September 2021
ER -