research-article

Should I Invest it?: Predicting Future Success of Yelp Restaurants

Authors:

Xiaopeng Lu,

Jiaming Qu,

Yongxing Jiang,

Yanbing ZhaoAuthors Info & Claims

PEARC '18: Proceedings of the Practice and Experience on Advanced Research Computing: Seamless Creativity

Article No.: 64, Pages 1 - 6

https://doi.org/10.1145/3219104.3229287

Published: 22 July 2018 Publication History

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Abstract

Online customer reviews are becoming more and more important in helping consumer make decisions. Therefore, it is also interesting to see whether consumer reviews can be used to predict future business success. In this paper, using yelp dataset from 2016, we aimed to predict if the restaurant will still open till 2017. We focused on multi-level feature selection and analyzed features that influence the most for the future success of restaurant. The balanced accuracy is 67.46%. The result shows that our text features failed to have significant indications for the future success of the restaurant, while non-text features, especially business features, do have strong correlation with future restaurant performance. Furthermore, we did error analysis on insignificant features and gave potential improvements.

References

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Feng, Jason., Kitade, Naho., & Ritter, Matthew. (2015). Determining Restaurant Success or Failure. Retrieved from http://www.cs.dartmouth.edu/~lorenzo/teaching/cs174/Archive/Winter2015/Projects/finals/fkr.pdf

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Camillo, A. A., Connolly, Daniel J., & Kim, W. G. (2008). Success and Failure in Northern California. Cornell Hospitality Quarterly, 49(4).

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Kong, Angela., Nguyen, Vivian., & Xu, Catherina. (2016). Predicting International Restaurant Success with Yelp. Retrieved from http://cs229.stanford.edu/proj2016spr/report/062.pdf

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Wang, Aileen., Zeng, William., & Zhang, Jessica. (2016). Predicting New Restaurant Success and Rating with Yelp. Retrieved from https://web.Stanford.edu/class/cs221/2017/restricted/p-final/wizeng/final.pdf

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Yelp Dataset. (2016, 2017). Retrieved from https://www.yelp.com/dataset

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Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. NIPS'13 Proceedings of the 26th International Conference on Neural Information Processing Systems, 3111--3119.

Digital Library

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Ahfierakis, M. (2018). Using Yelp Data to Predict Restaurant Closure -- Towards Data Science. Retrieved from https://towardsdatascience.com/using-yelp-data-to-predict-restaurant-closure-8aafa4f72ad6

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McCormick, C. (2016). Word2Vec Tutorial - The Skip-Gram Model. Retrieved from http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/

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Cited By

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Burnham TPhelan J(2023)Ordinaries 13: apparent spite & apparent altruismJournal of Bioeconomics10.1007/s10818-023-09341-x25:3(147-180)Online publication date: 25-Nov-2023
https://doi.org/10.1007/s10818-023-09341-x
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
Antelmi ACordasco GKamiński BPrałat PScarano VSpagnuolo CSzufel P(2019)SimpleHypergraphs.jl—Novel Software Framework for Modelling and Analysis of HypergraphsAlgorithms and Models for the Web Graph10.1007/978-3-030-25070-6_9(115-129)Online publication date: 6-Jul-2019
https://dl.acm.org/doi/10.1007/978-3-030-25070-6_9

Index Terms

Should I Invest it?: Predicting Future Success of Yelp Restaurants
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
2. Information systems
  1. Data management systems
    1. Information integration
      1. Wrappers (data mining)

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Information

Published In

PEARC '18: Proceedings of the Practice and Experience on Advanced Research Computing: Seamless Creativity

July 2018

652 pages

ISBN:9781450364461

DOI:10.1145/3219104

General Chair:
Sergiu Sanielevici
Pittsburgh Supercomputing Center

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 July 2018

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PEARC '18

PEARC '18: Practice and Experience in Advanced Research Computing

July 22 - 26, 2018

PA, Pittsburgh, USA

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PEARC '18 Paper Acceptance Rate 79 of 123 submissions, 64%;

Overall Acceptance Rate 133 of 202 submissions, 66%

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Cited By

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Burnham TPhelan J(2023)Ordinaries 13: apparent spite & apparent altruismJournal of Bioeconomics10.1007/s10818-023-09341-x25:3(147-180)Online publication date: 25-Nov-2023
https://doi.org/10.1007/s10818-023-09341-x
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
Antelmi ACordasco GKamiński BPrałat PScarano VSpagnuolo CSzufel P(2019)SimpleHypergraphs.jl—Novel Software Framework for Modelling and Analysis of HypergraphsAlgorithms and Models for the Web Graph10.1007/978-3-030-25070-6_9(115-129)Online publication date: 6-Jul-2019
https://dl.acm.org/doi/10.1007/978-3-030-25070-6_9

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Research Note---How Much Should You Invest in Each Customer Relationship? A Competitive Strategic Approach

Web-a-where: geotagging web content

Linguistic features for review helpfulness prediction