@inproceedings{sazzed-2021-hybrid,
title = "A Hybrid Approach of Opinion Mining and Comparative Linguistic Analysis of Restaurant Reviews",
author = "Sazzed, Salim",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.144",
pages = "1281--1288",
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 \textit{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, \textit{BanglaRestaurant} and \textit{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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sazzed-2021-hybrid">
<titleInfo>
<title>A Hybrid Approach of Opinion Mining and Comparative Linguistic Analysis of Restaurant Reviews</title>
</titleInfo>
<name type="personal">
<namePart type="given">Salim</namePart>
<namePart type="family">Sazzed</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-sep</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
</titleInfo>
<originInfo>
<publisher>INCOMA Ltd.</publisher>
<place>
<placeTerm type="text">Held Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">sazzed-2021-hybrid</identifier>
<location>
<url>https://aclanthology.org/2021.ranlp-1.144</url>
</location>
<part>
<date>2021-sep</date>
<extent unit="page">
<start>1281</start>
<end>1288</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T A Hybrid Approach of Opinion Mining and Comparative Linguistic Analysis of Restaurant Reviews
%A Sazzed, Salim
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 sep
%I INCOMA Ltd.
%C Held Online
%F sazzed-2021-hybrid
%X 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.
%U https://aclanthology.org/2021.ranlp-1.144
%P 1281-1288
Markdown (Informal)
[A Hybrid Approach of Opinion Mining and Comparative Linguistic Analysis of Restaurant Reviews](https://aclanthology.org/2021.ranlp-1.144) (Sazzed, RANLP 2021)
ACL