Characterizing non-chain restaurants’ Yelp star-ratings: Generalizable findings from a representative sample of Yelp reviews

Keller, Daniel; Kostromitina, Maria

doi:10.1016/j.ijhm.2019.102440

Cited by 23 publications

(22 citation statements)

References 27 publications

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“…Some of the topics generated in this study for FDEs coincide with the dimensions that Oh and Kim (2020) defined for the fine-dining ethnic food restaurant experience as food, view, location, ambiance, price and service. Moreover, the elements such as food, service, and atmosphere mentioned in the studies on 5-star ratings in restaurant reviews (Keller and Kostromitina, 2020;Mathayomchan and Taecharungroj, 2020) corresponded with staff, food experience and atmosphere generated by LDA in this study.…”

Section: Dimensional Salience Analysissupporting

confidence: 56%

“…Many studies have defined the important elements of customer satisfaction discussed in the reviews of the restaurant industry as food, taste, service, atmosphere, physical environment (Pantelidis, 2010;Zhang et al, 2014a), food quality, price and value, service quality (Yan et al, 2015), long wait time, exotic and creative cuisine, lack of cleanliness, rude attitude, portion size, outdoor ambiance (Kwon et al, 2020), feeling, place, time (Jia, 2019) and overall experience (Keller and Kostromitina, 2020).…”

Section: Customer Satisfaction and Delight In Online Restaurant Reviewsmentioning

confidence: 99%

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Discovery of factors affecting tourists' fine dining experiences at five-star hotel restaurants in Istanbul

Aktaş-Polat

Polat

2021

BFJ

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PurposeThe purpose of this study is to discover the factors affecting customer delight, satisfaction and dissatisfaction in fine dining experiences (FDEs).Design/methodology/approachOnline user generated 2,585 reviews on TripAdvisor for 46 five-star hotel restaurants operating in Istanbul were analyzed with the latent Dirichlet allocation (LDA) algorithm.FindingsLDA created nine, eight and seven topics for delight, satisfaction and dissatisfaction, respectively. The most salient topics for customer delight, satisfaction and dissatisfaction in FDEs are staff (17.3%), view (19%), and food quality (23%), respectively.Originality/valueThis study is one of the few studies investigating customer delight and satisfaction together. The study shows that FDEs can be analyzed with text mining techniques. Moreover, the study contributes to the literature on customer delight by adding staff topic as an antecedent.

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Section: Dimensional Salience Analysissupporting

confidence: 56%

Section: Customer Satisfaction and Delight In Online Restaurant Reviewsmentioning

confidence: 99%

Discovery of factors affecting tourists' fine dining experiences at five-star hotel restaurants in Istanbul

Aktaş-Polat

Polat

2021

BFJ

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“…Finally, we classified the hotel reviews into different datasets according to the hotel star ratings and review overall ratings. Following the studies in [9,91], we categorized the online reviews into four datasets, namely, two-star and below hotels (1-, 1.5-, 2-and 2.5-stars hotels), three-star hotels (3-and 3.5-stars hotels), four-star hotels (4-and 4.5-stars hotels) and five-star hotels according to the hotel star ratings. Review classification based on the review overall rating is controversial [18].…”

Section: Data Collecting and Processingmentioning

confidence: 99%

Exploring Bidirectional Performance of Hotel Attributes through Online Reviews Based on Sentiment Analysis and Kano-IPA Model

Chen

Zhong

et al. 2022

Applied Sciences

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As people increasingly make hotel booking decisions relying on online reviews, how to effectively improve customer ratings has become a major point for hotel managers. Online reviews serve as a promising data source to enhance service attributes in order to improve online bookings. This paper employs online customer ratings and textual reviews to explore the bidirectional performance (good performance in positive reviews and poor performance in negative reviews) of hotel attributes in terms of four hotel star ratings. Sentiment analysis and a combination of the Kano model and importance-performance analysis (IPA) are applied. Feature extraction and sentiment analysis techniques are used to analyze the bidirectional performance of hotel attributes in terms of four hotel star ratings from 1,090,341 online reviews of hotels in London collected from TripAdvisor.com (accessed on 4 January 2022). In particular, a new sentiment lexicon for hospitality domain is built from numerous online reviews using the PolarityRank algorithm to convert textual reviews into sentiment scores. The Kano-IPA model is applied to explain customers’ rating behaviors and prioritize attributes for improvement. The results provide determinants of high/low customer ratings to different star hotels and suggest that hotel attributes contributing to high/low customer ratings vary across hotel star ratings. In addition, this paper analyzed the Kano categories and priority rankings of six hotel attributes for each star rating of hotels to formulate improvement strategies. Theoretical and practical implications of these results are discussed in the end.

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“…Moreover, given its advanced screening system that identifies fake reviews, Yelp is considered a reliable social media platform (Yang et al, 2017). Yelp's provided data set includes over four million restaurant reviews from various North American regions for researchers to analyze for academic purposes (Keller and Kostromitina, 2020). Thus, this study selected reviews from the Yelp data set as the qualified data source.…”

Section: Data Collectionmentioning

confidence: 99%

Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making

Lee

Kwon

Back

2021

IJCHM

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Purpose Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data. Design/methodology/approach The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making. Findings By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity. Practical implications The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results. Originality/value To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

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Characterizing non-chain restaurants’ Yelp star-ratings: Generalizable findings from a representative sample of Yelp reviews

Cited by 23 publications

References 27 publications

Discovery of factors affecting tourists' fine dining experiences at five-star hotel restaurants in Istanbul

Discovery of factors affecting tourists' fine dining experiences at five-star hotel restaurants in Istanbul

Exploring Bidirectional Performance of Hotel Attributes through Online Reviews Based on Sentiment Analysis and Kano-IPA Model

Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making

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