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Location, location, location!: quantifying the true impact of location on business reviews using a Yelp dataset

Authors:

Abu Saleh Md. Tayeen,

Abderrahmen Mtibaa,

Satyajayant MisraAuthors Info & Claims

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 1081 - 1088

https://doi.org/10.1145/3341161.3345334

Published: 15 January 2020 Publication History

Abstract

Today, with the emergence of various business review sites such as Yelp, Trip Advisor, and Zomato, people can write reviews and provide an assessment (often as 1-5 score rating). The success of a business on the crowd-sourced review platform has taken the form of positive reviews and high star ratings (failure are associated with negative reviews and low star ratings). We often claim that location plays a major role in determining the success or the failure of a given business. This paper attempts to verify this claim and quantifies the impact of location, solely, on business success, using two data sets; a Yelp dataset for business information and reviews, and another Location dataset that gathers location-based information in a city or an area. We perform an empirical study to quantify the impact of (i) <u>relative</u> location to well known landmarks and (ii) <u>parameterized</u> location (such as cost of living in a given zip code), on the success of restaurants. In our study, we found that <u>parameterized</u> location using location characteristic parameters such as housing affordability correlate highly with restaurant success with more than 0.81 correlation ratio. We also observe that the closer the restaurant to a landmark (relative location) the more likelihood it succeeds.

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Cited By

Chang CJhang SYang YChang HChang Y(2024)Utilizing Skip-Gram for Restaurant Vector Creation and Its Application in the Selection of Ideal Restaurant LocationsSmart Grid and Internet of Things10.1007/978-3-031-55976-1_14(141-147)Online publication date: 15-Mar-2024
https://doi.org/10.1007/978-3-031-55976-1_14
Zámková MRojík SProkop MČinčalová SStolín R(2022)Czech Consumers’ Preference for Organic Products in Online Grocery Stores during the COVID-19 PandemicInternational Journal of Environmental Research and Public Health10.3390/ijerph19201331619:20(13316)Online publication date: 15-Oct-2022
https://doi.org/10.3390/ijerph192013316
Vallapuram AKwon YLee LXu FHui P(2022)Causal Analysis on the Anchor Store Effect in a Location-based Social Network2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM55673.2022.10068687(202-209)Online publication date: 10-Nov-2022
https://doi.org/10.1109/ASONAM55673.2022.10068687

Location, location, location!: quantifying the true impact of location on business reviews using a Yelp dataset
1. Information systems
  1. Information retrieval
  2. Information systems applications

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cover image ACM Conferences

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2019

1228 pages

ISBN:9781450368681

DOI:10.1145/3341161

Editors:
Francesca Spezzano
Boise State University
,
Wei Chen
Microsoft Research, China
,
Xiaokui Xiao
National University of Singapore, Singapore

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 January 2020

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National Science Foundation

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ASONAM '19

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ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining

August 27 - 30, 2019

British Columbia, Vancouver, Canada

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ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;

Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Chang CJhang SYang YChang HChang Y(2024)Utilizing Skip-Gram for Restaurant Vector Creation and Its Application in the Selection of Ideal Restaurant LocationsSmart Grid and Internet of Things10.1007/978-3-031-55976-1_14(141-147)Online publication date: 15-Mar-2024
https://doi.org/10.1007/978-3-031-55976-1_14
Zámková MRojík SProkop MČinčalová SStolín R(2022)Czech Consumers’ Preference for Organic Products in Online Grocery Stores during the COVID-19 PandemicInternational Journal of Environmental Research and Public Health10.3390/ijerph19201331619:20(13316)Online publication date: 15-Oct-2022
https://doi.org/10.3390/ijerph192013316
Vallapuram AKwon YLee LXu FHui P(2022)Causal Analysis on the Anchor Store Effect in a Location-based Social Network2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM55673.2022.10068687(202-209)Online publication date: 10-Nov-2022
https://doi.org/10.1109/ASONAM55673.2022.10068687
Tayeen AMtibaa AMisra SBiswal M(2021)Impact of Locational Factors on Business Ratings/Reviews: A Yelp and TripAdvisor StudyBig Data and Social Media Analytics10.1007/978-3-030-67044-3_2(25-49)Online publication date: 19-Jan-2021
https://doi.org/10.1007/978-3-030-67044-3_2

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