Yelp Uygulamasındaki Restoranların Keşifsel Veri Analizi ile İncelenmesi

Yıl 2024, Cilt: 8 Sayı: 1, 31 – 40, 30.06.2024

Betül Ağaoğlu (cebe) Esra Özata Şahin

https://doi.org/10.30625/ijctr.1452507

Öz

Sosyal medyanın gelişmesi, insanların ürünler ve bunları sağlayan işletmeler hakkında başkalarıyla iletişim kurmasını kolaylaştırmıştır. Bu gelişme, tüketicilerin restoranlar hakkında yorum yapmasına olanak tanırken işletmeler açısından da performans üzerinde oldukça etkili hale gelmiştir. Tüketicilerin çevrimiçi yapmış oldukları bu değerlendirmeler restoran tercihinde karar verme sürecini etkilerken aynı zamanda restoran performansını da önemli ölçüde etkilediği ifade edilmiştir. Dolayısıyla restoranları değerlendiren uygulamalar dikkat çekmeye başlamıştır. Bu uygulamalarla beraber tüketiciler aldığı hizmeti puanlama, değerlendirme ve yorumlama fırsatı bulmaktadır. İşletmeler ise bu tür uygulamaları ciddi bir rekabet aracı olarak görmekte ve takip etmektedir. Restoranları değerlendiren uygulamalardan biri olan Yelp, San Francisco-Kaliforniya merkezli olup 2004 yılında kurulmuştur. Halka açık bir Amerikan şirketidir. Bu şirket, işletmeler yani restoranlar hakkında incelemeler yayınlayan Yelp.com web sitesini ve aynı zamanda Yelp mobil uygulamasını geliştirmiştir. Bu uygulamada kullanıcılar işletmeyle ilgili bir ile 5 yıldız derecelendirme sistemi kullanarak aldığı hizmetin değerlendirmesini yapabilmektedir. Yelp 2021 yılında, çeşitli data setlerinin yayınlandığı “www.kaggle.com” (veri bilimciler ve makine öğrenimi uygulayıcılarından oluşan çevrimiçi topluluk) üzerinden veri paylaşımında bulunmuştur. Bu noktadan hareketle araştırmanın amacı, Yelp uygulamasındaki restoranların Keşifsel Veri Analizi kullanılarak incelenmesidir. Keşifsel Veri Analizi ise, genellikle istatistiksel grafikler ve diğer veri görselleştirme yöntemlerini kullanarak temel özelliklerini özetlemek için veri kümelerini analiz etme yaklaşımıdır. Araştırmanın veri setini ise kaggle’da yer alan data seti oluşturmaktadır. Sonuçlara bakıldığında, kullanıcıların yıllar üzerindeki yorum artış miktarı analiz edilerek pandemi etkisi fark edilmiştir. Restoran incelemesinde “great, love, amazing, awesome ve bad” yorumlarda kullanılan en sık kelimeler olarak görülmektedir.

Anahtar Kelimeler

Yelp, EDA, Keşifsel Veri Analizi, Restoranların Değerlendirilmesi, Python

Kaynakça

• AAA Diamonds (2022). Erişim Linki: https://www.aaa.com/diamonds/$ , Erişim Tarihi: 19.09.2022.
• Anderson, M., & Magruder, J. (2012). Learning from the crowd: Regression discontinuity estimates of the effects of an online review database. The Economic Journal, 122(563), 957-989.
• Antonio, N., de Almeida, A. M., Nunes, L., Batista, F., & Ribeiro, R. (2018). Hotel online reviews: creating a multi-source aggregated index. International Journal of Contemporary Hospitality Management.
• Asani, E., Vahdat-Nejad, H., & Sadri, J. (2021). Restaurant recommender system based on sentiment analysis. Machine Learning with Applications, 6, 100114.
• Asghar, N. (2016). Yelp dataset challenge: Review rating prediction. arXiv preprint arXiv:1605.05362.
• Bilir, Z. (2020). İşte Dünyanın En İtibarlı Restoran Derecelendirme Sistemleri. Erişim Linki: https://www.turizmgunlugu.com/2020/05/04/restoran-derecelendirme-sistemleri/ , Erişim Tarihi: 7.09.2022.
• Chiang, C. F., & Guo, H. W. (2021). Consumer perceptions of the Michelin Guide and attitudes toward Michelin-starred restaurants. International Journal of Hospitality Management, 93, 102793.
• Chowdhury, D., Hovda, S., & Lund, B. (2023). Analysis of cuttings concentration experimental data using exploratory data analysis. Geoenergy Science and Engineering, 221, 111254.
• Deng, L., Xu, D., Ye, Q., & Sun, W. (2022). Food culture and online rating behavior. Electronic Commerce Research and Applications, 52, 101128.
• Forbes Travel Guide (2022). Erişim Linki: https://www.forbestravelguide.com/about , Erişim Tarihi: 19.09.2022.
• Gault ve Millau (2022). Erişim Linki: https://tr.frwiki.wiki/wiki/Gault_et_Millau#:~:text=Le%20Gault%20et%20Millau%20(veya,kurulan%20bir%20Frans%C4%B1z%20gastronomi%20rehberidir%20 , Erişim Tarihi: 19.09.2022.
• Hajek, P., & Sahut, J. M. (2022). Mining behavioural and sentiment-dependent linguistic patterns from restaurant reviews for fake review detection. Technological Forecasting and Social Change, 177(C).
• Kaggle (2022). Erişim Linki: https://en.wikipedia.org/wiki/Kaggle , Erişim Tarihi: 20.09.2022. Keşifsel Veri Analizi (2022). Erişim Linki: https://tr.wikipedia.org/wiki/Ke%C5%9Fifsel_veri_analizi , Erişim Tarihi: 20.09.2022.
• Kostromitina, M., Keller, D., Cavusoglu, M., & Beloin, K. (2021). “His lack of a mask ruined everything.” Restaurant customer satisfaction during the COVID-19 outbreak: An analysis of Yelp review texts and star-ratings. International Journal of Hospitality Management, 98, 103048.
• Liu, C. H. S., Su, C. S., Gan, B., & Chou, S. F. (2014). Effective restaurant rating scale development and a mystery shopper evaluation approach. International Journal of Hospitality Management, 43, 53-64.
• Luo, Y., & Xu, X. (2021). Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic. International Journal of Hospitality Management, 94, 102849.
• Michelin Guide. (2022). Erişim Linki: https://guide.michelin.com/en/about-us , Erişim Tarihi: 7.09.2022.
• Nakayama, M., & Wan, Y. (2018). Is culture of origin associated with more expressions? An analysis of Yelp reviews on Japanese restaurants. Tourism Management, 66, 329-338.
• Qiu, J., Liu, C., Li, Y., & Lin, Z. (2018). Leveraging sentiment analysis at the aspects level to predict ratings of reviews. Information Sciences, 451, 295-309.
• Shan, G., Zhang, D., Zhou, L., Suo, L., Lim, J., & Shi, C. (2018, August). Inconsistency investigation between online review content and ratings. In Twenty-fourth Americas Conference on Information Systems.
• Tian, G., Lu, L., & McIntosh, C. (2021). What factors affect consumers’ dining sentiments and their ratings: Evidence from restaurant online review data. Food Quality and Preference, 88, 104060.
• Wang, Y., Kim, J., & Kim, J. (2021). The financial impact of online customer reviews in the restaurant industry: A moderating effect of brand equity. International Journal of Hospitality Management, 95, 102895.
• World’s 50 Best Restaurants (2022). Erişim Linki: https://www.theworlds50best.com/about , Erişim Tarihi: 19.09.2022.
• YEDY (2022). Erişim Linki: http://www.yedy.com.tr/ , Erişim Tarihi: 19.09.2022.
• Yelp Dataset (2022). Erişim Linki: http://www.yelp.com/ , Erişim Tarihi: 20.09.2022.
• Yoo, T., & Suh, K. H. (2022). Experts vs. the public in the evaluation of restaurants: A business ecosystem approach. International Journal of Hospitality Management, 105, 103265.
• Zagat. (2022). Erişim Linki: https://stories.zagat.com/pages/about , Erişim Tarihi: 7.09.2022.

Investıgatıon of Restaurants In Yelp Applıcatıon Wıth Exploratory Data Analysis

Yıl 2024, Cilt: 8 Sayı: 1, 31 – 40, 30.06.2024

Betül Ağaoğlu (cebe) Esra Özata Şahin

https://doi.org/10.30625/ijctr.1452507

Öz

The development of social media has made it easier for people to communicate with others about products and the businesses that provide them. While this development allows consumers to comment on restaurants, it has also become very effective on the performance of businesses. It has been stated that these online evaluations made by consumers affect the decision-making process in restaurant selection and also significantly affect restaurant performance. Therefore, applications that evaluate restaurants have begun to attract attention. With these applications, consumers have the opportunity to rate, evaluate and comment on the service they receive. Businesses see such practices as a serious competitive tool and follow them. Yelp, one of the applications that evaluate restaurants, is based in San Francisco-California and was founded in 2004. It is a publicly traded American company. This company developed the website Yelp.com, which publishes reviews of businesses, namely restaurants, as well as the Yelp mobile app. In this application, users can evaluate the service they receive by using a one to 5 star rating system. In 2021, Yelp shared data through “www.kaggle.com” (an online community of data scientists and machine learning practitioners), where various data sets were published. Starting from this point, the purpose of the research is to examine the restaurants in the Yelp application using Exploratory Data Analysis. Exploratory Data Analysis is an approach to analyzing data sets to summarize their key features, often using statistical graphs and other data visualization methods. The data set of the research is the data set in Kaggle. Looking at the results, the pandemic effect was noticed by analyzing the increase in users’ comments over the years. In restaurant reviews, “great, love, amazing, awesome and bad” are seen as the most frequently used words in the comments.

Anahtar Kelimeler

Yelp, EDA, Exploratory Data Analysis, Evaluation of Restaurants, Python

Kaynakça

• AAA Diamonds (2022). Erişim Linki: https://www.aaa.com/diamonds/$ , Erişim Tarihi: 19.09.2022.
• Anderson, M., & Magruder, J. (2012). Learning from the crowd: Regression discontinuity estimates of the effects of an online review database. The Economic Journal, 122(563), 957-989.
• Antonio, N., de Almeida, A. M., Nunes, L., Batista, F., & Ribeiro, R. (2018). Hotel online reviews: creating a multi-source aggregated index. International Journal of Contemporary Hospitality Management.
• Asani, E., Vahdat-Nejad, H., & Sadri, J. (2021). Restaurant recommender system based on sentiment analysis. Machine Learning with Applications, 6, 100114.
• Asghar, N. (2016). Yelp dataset challenge: Review rating prediction. arXiv preprint arXiv:1605.05362.
• Bilir, Z. (2020). İşte Dünyanın En İtibarlı Restoran Derecelendirme Sistemleri. Erişim Linki: https://www.turizmgunlugu.com/2020/05/04/restoran-derecelendirme-sistemleri/ , Erişim Tarihi: 7.09.2022.
• Chiang, C. F., & Guo, H. W. (2021). Consumer perceptions of the Michelin Guide and attitudes toward Michelin-starred restaurants. International Journal of Hospitality Management, 93, 102793.
• Chowdhury, D., Hovda, S., & Lund, B. (2023). Analysis of cuttings concentration experimental data using exploratory data analysis. Geoenergy Science and Engineering, 221, 111254.
• Deng, L., Xu, D., Ye, Q., & Sun, W. (2022). Food culture and online rating behavior. Electronic Commerce Research and Applications, 52, 101128.
• Forbes Travel Guide (2022). Erişim Linki: https://www.forbestravelguide.com/about , Erişim Tarihi: 19.09.2022.
• Gault ve Millau (2022). Erişim Linki: https://tr.frwiki.wiki/wiki/Gault_et_Millau#:~:text=Le%20Gault%20et%20Millau%20(veya,kurulan%20bir%20Frans%C4%B1z%20gastronomi%20rehberidir%20 , Erişim Tarihi: 19.09.2022.
• Hajek, P., & Sahut, J. M. (2022). Mining behavioural and sentiment-dependent linguistic patterns from restaurant reviews for fake review detection. Technological Forecasting and Social Change, 177(C).
• Kaggle (2022). Erişim Linki: https://en.wikipedia.org/wiki/Kaggle , Erişim Tarihi: 20.09.2022. Keşifsel Veri Analizi (2022). Erişim Linki: https://tr.wikipedia.org/wiki/Ke%C5%9Fifsel_veri_analizi , Erişim Tarihi: 20.09.2022.
• Kostromitina, M., Keller, D., Cavusoglu, M., & Beloin, K. (2021). “His lack of a mask ruined everything.” Restaurant customer satisfaction during the COVID-19 outbreak: An analysis of Yelp review texts and star-ratings. International Journal of Hospitality Management, 98, 103048.
• Liu, C. H. S., Su, C. S., Gan, B., & Chou, S. F. (2014). Effective restaurant rating scale development and a mystery shopper evaluation approach. International Journal of Hospitality Management, 43, 53-64.
• Luo, Y., & Xu, X. (2021). Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic. International Journal of Hospitality Management, 94, 102849.
• Michelin Guide. (2022). Erişim Linki: https://guide.michelin.com/en/about-us , Erişim Tarihi: 7.09.2022.
• Nakayama, M., & Wan, Y. (2018). Is culture of origin associated with more expressions? An analysis of Yelp reviews on Japanese restaurants. Tourism Management, 66, 329-338.
• Qiu, J., Liu, C., Li, Y., & Lin, Z. (2018). Leveraging sentiment analysis at the aspects level to predict ratings of reviews. Information Sciences, 451, 295-309.
• Shan, G., Zhang, D., Zhou, L., Suo, L., Lim, J., & Shi, C. (2018, August). Inconsistency investigation between online review content and ratings. In Twenty-fourth Americas Conference on Information Systems.
• Tian, G., Lu, L., & McIntosh, C. (2021). What factors affect consumers’ dining sentiments and their ratings: Evidence from restaurant online review data. Food Quality and Preference, 88, 104060.
• Wang, Y., Kim, J., & Kim, J. (2021). The financial impact of online customer reviews in the restaurant industry: A moderating effect of brand equity. International Journal of Hospitality Management, 95, 102895.
• World’s 50 Best Restaurants (2022). Erişim Linki: https://www.theworlds50best.com/about , Erişim Tarihi: 19.09.2022.
• YEDY (2022). Erişim Linki: http://www.yedy.com.tr/ , Erişim Tarihi: 19.09.2022.
• Yelp Dataset (2022). Erişim Linki: http://www.yelp.com/ , Erişim Tarihi: 20.09.2022.
• Yoo, T., & Suh, K. H. (2022). Experts vs. the public in the evaluation of restaurants: A business ecosystem approach. International Journal of Hospitality Management, 105, 103265.
• Zagat. (2022). Erişim Linki: https://stories.zagat.com/pages/about , Erişim Tarihi: 7.09.2022.

Toplam 27 adet kaynakça vardır.

Ayrıntılar

Kaynak Göster

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