IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i19p5254-d270452.html
   My bibliography  Save this article

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

Author

Listed:
  • Yi Luo

    (College of Business Administration, Capital University of Economics and Business, Beijing 100070, China)

  • Xiaowei Xu

    (School of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China)

Abstract

Helpful online reviews could be utilized to create sustainable marketing strategies in the restaurant industry, which contributes to national sustainable economic development. This study, the main aspects (including food/taste, experience, location, and value) from 294,034 reviews on Yelp.com were extracted empirically using the Latent Dirichlet Allocation (LDA) and positive and negative sentiment were assigned to each extracted aspect. Positive sentiments were associated with food/taste, while negative sentiments were associated with value. This study further proves a robust classification algorithm based on Support Vector Machine (SVM) with a Fuzzy Domain Ontology (FDO) algorithm outperforms other traditional classification algorithms such as Naïve Bayes (MB) and SVM ontology in predicting the helpfulness of online reviews. This study enriches the literature on managerial aspects of sustainability by analyzing a large amount of plain text data that customers generated. The results of this study could be used as sustainable marketing strategy for review website developers to design sophisticated, intelligence review systems by enabling customers to sort and filter helpful reviews based on their preferences. The extracted aspects and their assigned sentiment could also help restaurateurs better understand how to meet diverse customers’ needs and maintain sustainable competitive advantages.

Suggested Citation

  • Yi Luo & Xiaowei Xu, 2019. "Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5254-:d:270452
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/19/5254/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/19/5254/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Zhiwei & Park, Sangwon, 2015. "What makes a useful online review? Implication for travel product websites," Tourism Management, Elsevier, vol. 47(C), pages 140-151.
    2. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Grenoble Ecole de Management (Post-Print) halshs-01923243, HAL.
    3. Susan (Sixue) Jia, 2018. "Leisure Motivation and Satisfaction: A Text Mining of Yoga Centres, Yoga Consumers, and Their Interactions," Sustainability, MDPI, vol. 10(12), pages 1-17, November.
    4. Yuanyuan Guo & Yanqing Wang & Chaoyou Wang, 2019. "Exploring the Salient Attributes of Short-Term Rental Experience: An Analysis of Online Reviews from Chinese Guests," Sustainability, MDPI, vol. 11(16), pages 1-19, August.
    5. Yoon-Joo Park, 2018. "Predicting the Helpfulness of Online Customer Reviews across Different Product Types," Sustainability, MDPI, vol. 10(6), pages 1-20, May.
    6. Fang, Bin & Ye, Qiang & Kucukusta, Deniz & Law, Rob, 2016. "Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics," Tourism Management, Elsevier, vol. 52(C), pages 498-506.
    7. Xiangbin Yan & Jing Wang & Michael Chau, 2015. "Customer revisit intention to restaurants: Evidence from online reviews," Information Systems Frontiers, Springer, vol. 17(3), pages 645-657, June.
    8. Seungju Nam & Chunghun Ha & Hyun Cheol Lee, 2018. "Redesigning In-Flight Service with Service Blueprint Based on Text Analysis," Sustainability, MDPI, vol. 10(12), pages 1-21, November.
    9. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Post-Print hal-03511272, HAL.
    10. Pan, Yue & Zhang, Jason Q., 2011. "Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews," Journal of Retailing, Elsevier, vol. 87(4), pages 598-612.
    11. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    12. Malhotra, Naresh K, 1984. "Reflections on the Information Overload Paradigm in Consumer Decision Making [Perspectives on Information Overload]," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 10(4), pages 436-440, March.
    13. Sparks, Beverley A. & Browning, Victoria, 2011. "The impact of online reviews on hotel booking intentions and perception of trust," Tourism Management, Elsevier, vol. 32(6), pages 1310-1323.
    14. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type," Post-Print halshs-01923243, HAL.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Feng, Yunting & Lai, Kee-hung & Zhu, Qinghua, 2022. "Green supply chain innovation: Emergence, adoption, and challenges," International Journal of Production Economics, Elsevier, vol. 248(C).
    2. Arielle Moro & Adrian Holzer, 2020. "A Framework to Predict Consumption Sustainability Levels of Individuals," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
    3. Akbari, Morteza & Foroudi, Pantea & Zaman Fashami, Rahime & Mahavarpour, Nasrin & Khodayari, Maryam, 2022. "Let us talk about something: The evolution of e-WOM from the past to the future," Journal of Business Research, Elsevier, vol. 149(C), pages 663-689.
    4. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    5. Sergio M. Fernández-Miguélez & Miguel Díaz-Puche & Juan A. Campos-Soria & Federico Galán-Valdivieso, 2020. "The Impact of Social Media on Restaurant Corporations’ Financial Performance," Sustainability, MDPI, vol. 12(4), pages 1-14, February.
    6. Manuel A. Fernández-Gámez & José António C. Santos & Julio Diéguez-Soto & Juan A. Campos-Soria, 2020. "The Effect of Countries’ Health and Environmental Conditions on Restaurant Reputation," Sustainability, MDPI, vol. 12(23), pages 1-14, December.
    7. Albérico Travassos Rosário & Joana Carmo Dias & Hélder Ferreira, 2023. "Bibliometric Analysis on the Application of Fuzzy Logic into Marketing Strategy," Businesses, MDPI, vol. 3(3), pages 1-22, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Moradi, Masoud & Dass, Mayukh & Kumar, Piyush, 2023. "Differential effects of analytical versus emotional rhetorical style on review helpfulness," Journal of Business Research, Elsevier, vol. 154(C).
    2. Filieri, Raffaele & Lin, Zhibin & Pino, Giovanni & Alguezaui, Salma & Inversini, Alessandro, 2021. "The role of visual cues in eWOM on consumers’ behavioral intention and decisions," Journal of Business Research, Elsevier, vol. 135(C), pages 663-675.
    3. Cai, Xiaowei & Cebollada, Javier & Cortiñas, Mónica, 2023. "Impact of seller- and buyer-created content on product sales in the electronic commerce platform: The role of informativeness, readability, multimedia richness, and extreme valence," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    4. Elvira Ismagilova & Emma L. Slade & Nripendra P. Rana & Yogesh K. Dwivedi, 2020. "The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis," Information Systems Frontiers, Springer, vol. 22(5), pages 1203-1226, October.
    5. Yi Feng & Yunqiang Yin & Dujuan Wang & Lalitha Dhamotharan & Joshua Ignatius & Ajay Kumar, 2023. "Diabetic patient review helpfulness: unpacking online drug treatment reviews by text analytics and design science approach," Annals of Operations Research, Springer, vol. 328(1), pages 387-418, September.
    6. Elvira Ismagilova & Emma L. Slade & Nripendra P. Rana & Yogesh K. Dwivedi, 0. "The Effect of Electronic Word of Mouth Communications on Intention to Buy: A Meta-Analysis," Information Systems Frontiers, Springer, vol. 0, pages 1-24.
    7. Sunyoung Hlee & Hanna Lee & Chulmo Koo, 2018. "Hospitality and Tourism Online Review Research: A Systematic Analysis and Heuristic-Systematic Model," Sustainability, MDPI, vol. 10(4), pages 1-27, April.
    8. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    9. Jin Li & Yulan Zhang & Jianping Li & Jiangze Du, 2023. "The Role of Sentiment Tendency in Affecting Review Helpfulness for Durable Products: Nonlinearity and Complementarity," Information Systems Frontiers, Springer, vol. 25(4), pages 1459-1477, August.
    10. Kim, Taeyong & Hwang, Seungsoo & Kim, Minkyung, 2022. "Text analysis of online customer reviews for products in the FCB quadrants: Procedure, outcomes, and implications," Journal of Business Research, Elsevier, vol. 150(C), pages 676-689.
    11. Sangjae Lee & Kun Chang Lee & Joon Yeon Choeh, 2020. "Using Bayesian Network to Predict Online Review Helpfulness," Sustainability, MDPI, vol. 12(17), pages 1-17, August.
    12. Li, Yuanshuo & Zhang, Zili & Pedersen, Susanne & Liu, Xudong & Zhang, Ziqiong, 2023. "The influence of relative popularity on negative fake reviews: A case study on restaurant reviews," Journal of Business Research, Elsevier, vol. 162(C).
    13. Seungju Nam & Hyun Cheol Lee, 2019. "A Text Analytics-Based Importance Performance Analysis and Its Application to Airline Service," Sustainability, MDPI, vol. 11(21), pages 1-24, November.
    14. Baidyanath Biswas & Pooja Sengupta & Boudhayan Ganguly, 2022. "Your reviews or mine? Exploring the determinants of “perceived helpfulness” of online reviews: a cross-cultural study," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1083-1102, September.
    15. Wu, Laurie & Shen, Han & Fan, Alei & Mattila, Anna S., 2017. "The impact of language style on consumers′ reactions to online reviews," Tourism Management, Elsevier, vol. 59(C), pages 590-596.
    16. Book, Laura A. & Tanford, Sarah & Chang, Wen, 2018. "Customer reviews are not always informative: The impact of effortful versus heuristic processing," Journal of Retailing and Consumer Services, Elsevier, vol. 41(C), pages 272-280.
    17. Yani Wang & Jun Wang & Tang Yao, 2019. "What makes a helpful online review? A meta-analysis of review characteristics," Electronic Commerce Research, Springer, vol. 19(2), pages 257-284, June.
    18. Raffaele Filieri & Elisabetta Raguseo & Claudio Vitari, 2018. "What moderates the influence of extremely negative ratings? The role of review and reviewer characteristics," Post-Print halshs-01923196, HAL.
    19. Meek, Stephanie & Wilk, Violetta & Lambert, Claire, 2021. "A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews," Journal of Business Research, Elsevier, vol. 125(C), pages 354-367.
    20. Taekyung Kim & Hwirim Jo & Yerin Yhee & Chulmo Koo, 2022. "Robots, artificial intelligence, and service automation (RAISA) in hospitality: sentiment analysis of YouTube streaming data," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 259-275, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5254-:d:270452. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.