Michelin Yıldızlı Restoranlarda Yemek Deneyimi ve Duygu Yönelimleri: Türkiye'deki Dijital Müşteri Yorumları Üzerine Bir İnceleme

Öz

Bu çalışma, Türkiye'deki Michelin yıldızlı restoranlarda yaşanan yemek deneyiminin dijital müşteri yorumlarında hangi bileşenler üzerinden ve hangi duygusal yönelimlerle ifade edildiğini incelemeyi amaçlamaktadır. Araştırma kapsamında İstanbul, İzmir, Muğla ve Nevşehir'de faaliyet gösteren 15 Michelin yıldızlı restorana 1.801 Google Haritalar kullanıcı yorumu analiz edilmiştir. Veriler duygu analizi ve tematik analiz birlikte kullanılarak değerlendirilmiştir. Bulgular, müşteri yorumlarında yemek kalitesi, servis, fiziksel ortam, ekonomik değer ve prestij gibi boyutların birbirinden bağımsız değil, birbiriyle ilişkilendirilerek ele alındığını göstermektedir. Şef deneyimi, servis süreci ve fiziksel ortam temaları arasındaki yüksek birlikte görülme değerleri bu bulguyu desteklemektedir. Servis deneyimi ve misafirperverlik teması en yüksek pozitif duygu skorunu üretirken, hizmet hataları negatif duygu yönelimi gösteren tek tema olmuştur. Ekonomik değer teması ise nötr düzeye yakın bir yapı sergilemiştir. Elde edilen bulgular, Michelin yıldızlı restoran deneyiminin hem işlevsel hem de sembolik boyutlarla değerlendirildiğini ortaya koymaktadır.

Anahtar Kelimeler

• Duygu analizi
• tematik analiz
• çevrim içi müşteri yorumları
• deneyim boyutları
• sembolik tüketim

Dining Experience and Sentiment Orientations in Michelin-Starred Restaurants: An Examination of Digital Customer Reviews in Türkiye

Öz

This study aims to examine the components through which dining experiences at Michelin-starred restaurants in Türkiye are expressed in digital customer reviews and the sentiment orientations associated with these experiences. The study analyzed 1,801 Google Maps user reviews of 15 Michelin-starred restaurants operating in İstanbul, İzmir, Muğla, and Nevşehir. The data were evaluated through the combined use of sentiment analysis and thematic analysis. The findings indicate that dimensions such as food quality, service, physical environment, economic value, and prestige are not addressed independently in customer reviews but are evaluated in relation to one another. The high co-occurrence values among the themes of chef experience, service process, and physical environment support this finding. The service experience and hospitality theme produced the highest positive sentiment score, whereas service failures constituted the only theme displaying a negative sentiment orientation. The economic value theme, by contrast, exhibited a structure close to the neutral level. The findings demonstrate that dining experiences at Michelin-starred restaurants are evaluated through both functional and symbolic dimensions.

Anahtar Kelimeler

• Sentiment analysis
• thematic analysis
• online customer reviews
• experience dimensions
• symbolic consumption

Kaynakça

1. Abdullah, S., Van Cauwenberge, P., Vander Bauwhede, H. ve O'Connor, P. (2024). Review ratings, sentiment in review comments, and restaurant profitability: Firm-level evidence. Cornell Hospitality Quarterly, 65(3), 378–392. https://doi.org/10.1177/19389655231214758
2. Akhoondnejad, A. (2025). During-experience involvement and dining experience. Tourism and Hospitality Research, 25(4), 621–635. https://doi.org/10.1177/14673584241259972
3. Aktas-Polat, S. ve Polat, S. (2022). Discovery of factors affecting tourists’ fine dining experiences at five-star hotel restaurants in Istanbul. British Food Journal, 124(1), 221–238. https://doi.org/10.1108/BFJ-02-2021-0138
4. Alaei, A. R., Becken, S. ve Stantic, B. (2019). Sentiment analysis in tourism: Capitalizing on big data. Journal of Travel Research, 58(2), 175–191. https://doi.org/10.1177/0047287517747753
5. Bang, D. ve Jang, S. S. (2026). Platform endorsements as drivers of consumer behavior: A quasi-experimental study using restaurant review data. International Journal of Hospitality Management, 133, Article 104502. https://doi.org/10.1016/j.ijhm.2025.104502
6. Bang, D., Choi, K. ve Kim, A. J. (2022). Does Michelin effect exist? An empirical study on the effects of Michelin stars. International Journal of Contemporary Hospitality Management, 34(6), 2298–2319. https://doi.org/10.1108/IJCHM-08-2021-1025
7. Barrera-Barrera, R. (2023). Identifying the attributes of consumer experience in Michelin-starred restaurants: A text-mining analysis of online customer reviews. British Food Journal, 125(13), 579–598. https://doi.org/10.1108/BFJ-05-2023-0408
8. Batat, W. (2021). The role of luxury gastronomy in culinary tourism: An ethnographic study of Michelin-starred restaurants in France. International Journal of Tourism Research, 23(2), 150–163. https://doi.org/10.1002/jtr.2372

9. Baykal, M. ve Yazıcı Ayyıldız, A. (2024). İstanbul ve Londra’daki Michelin yıldızlı restoranlara yönelik müşteri şikâyetlerinin karşılaştırılması. Journal of Tourism and Gastronomy Studies, 12(2), 850–868. https://doi.org/10.21325/jotags.2024.1415
10. Bilgihan, A., Seo, S. ve Choi, J. (2018). Identifying restaurant satisfiers and dissatisfiers: Suggestions from online reviews. Journal of Hospitality Marketing and Management, 27(5), 601–625. https://doi.org/10.1080/19368623.2018.1396275
11. Birim, Ş. Ö. ve Kazancıoğlu, İ. (2026). Decoding customer satisfaction in Michelin Green Star restaurants: BERTopic modelling and sentiment analysis. British Food Journal, 128(4), 1274–1313. https://doi.org/10.1108/BFJ-07-2025-0883
12. Braun, V. ve Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
13. Braun, V. ve Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806
14. Cao, Y., Li, X. R., DiPietro, R. ve So, K. K. F. (2019). The creation of memorable dining experiences: Formative index construction. International Journal of Hospitality Management, 82, 308–317. https://doi.org/10.1016/j.ijhm.2018.10.010
15. Castillo-Manzano, J. I., Castro-Nuño, M., López-Valpuesta, L. ve Zarzoso, Á. (2021). Quality versus quantity: An assessment of the impact of Michelin-starred restaurants on tourism in Spain. Tourism Economics, 27(5), 1166–1174. https://doi.org/10.1177/1354816620917482
16. Chang, Y.-Y., Cheng, C.-C., Tsai, M.-C. ve Xie, P.-W. (2025). Bridging the knowledge gap of memorable dining experience at Michelin-starred restaurants: insights from a new two-dimensional strategic matrix. British Food Journal, 127(6), 2035–2064. https://doi.org/10.1108/BFJ-12-2023-1066
17. Chen, A., Peng, N. ve Hung, K.-P. (2015). The effects of luxury restaurant environments on diners’ emotions and loyalty: Incorporating diner expectations into an extended Mehrabian–Russell model. International Journal of Contemporary Hospitality Management, 27(2), 236–260. https://doi.org/10.1108/IJCHM-07-2013-0280
18. Chiang, C.-F. ve Guo, H.-W. (2021). Consumer perceptions of the Michelin Guide and attitudes toward Michelin-starred restaurants. International Journal of Hospitality Management, 93, Article 102793. https://doi.org/10.1016/j.ijhm.2020.102793
19. Chua, B.-L., Jin, N. P., Lee, S. ve Goh, B. (2014). Influence of mechanic, functional, and humanic clues on customers’ experiential values and behavioral intentions in full-service restaurants. Journal of Foodservice Business Research, 17(2), 67–84. https://doi.org/10.1080/15378020.2014.902641
20. Clemes, M. D., Mohi, Z., Li, X. ve Hu, B. (2018). Synthesizing moderate upscale restaurant patrons’ dining experiences. Asia Pacific Journal of Marketing and Logistics, 30(3), 627–651. https://doi.org/10.1108/APJML-06-2017-0115
21. Creswell, J. W. ve Plano Clark, V. L. (2007). Designing and conducting mixed methods research. Thousand Oaks: Sage.
22. Dang, N. C., Moreno-García, M. N. ve De la Prieta, F. (2020). Sentiment analysis based on deep learning: A comparative study. Electronics, 9(3), Article 483. https://doi.org/10.3390/electronics9030483
23. Daries, N., Cristobal-Fransi, E., Sánchez-García, J. ve Marine-Roig, E. (2024). Customers’ behavioral intentions when visiting upscale restaurants: Enjoying the experience or posturing? International Journal of Gastronomy and Food Science, 35, Article 100840. https://doi.org/10.1016/j.ijgfs.2023.100840
24. Dehkharghani, R., Saygin, Y., Yanikoglu, B. ve Oflazer, K. (2016). SentiTurkNet: A Turkish polarity lexicon for sentiment analysis. Language Resources and Evaluation, 50(3), 667–685. https://doi.org/10.1007/s10579-015-9307-6
25. Devlin, J., Chang, M.-W., Lee, K. ve Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. J. Burstein, C. Doran, T. Solorio (Ed.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Long and Short Papers) içinde (Cilt 1, s. 4171–4186). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423
26. Doppler, S., Steffen, A. ve Wurzer, L.-M. (2020). Event catering: Enhancing customer satisfaction by creating memorable holistic food experiences. S. Doppler ve A. Steffen (Ed.), Case studies on food experiences in marketing, retail, and events içinde (s. 133–145). Woodhead Publishing. https://doi.org/10.1016/B978-0-12-817792-1.00011-3
27. Feldman, Z. (2021). “Good food” in an Instagram age: Rethinking hierarchies of culture, criticism and taste. European Journal of Cultural Studies, 24(6), 1340–1359. https://doi.org/10.1177/13675494211055733
28. Gräbner, D., Zanker, M., Fliedl, G. ve Fuchs, M. (2012). Classification of customer reviews based on sentiment analysis. M. Fuchs, F. Ricci ve L. Cantoni (Ed.), Information and communication technologies in tourism 2012 içinde (s. 460–470). Springer. https://doi.org/10.1007/978-3-7091-1142-0_40
29. Haddi, E., Liu, X. ve Shi, Y. (2013). The role of text pre-processing in sentiment analysis. Procedia Computer Science, 17, 26–32. https://doi.org/10.1016/j.procs.2013.05.005
30. Horng, J.-S. ve Hsu, H. (2021). Esthetic dining experience: The relations among aesthetic stimulation, pleasantness, memorable experience, and behavioral intentions. Journal of Hospitality Marketing and Management, 30(4), 419–437. https://doi.org/10.1080/19368623.2021.1859425
31. Hussein, A. S. (2018). Revisiting the importance of casual dining experience quality: An empirical study. International Journal of Quality and Service Sciences, 10(3), 233–252. https://doi.org/10.1108/IJQSS-04-2017-0041
32. Hyang, O. M. (2019). Dimensions of restaurant customer experience and emotions: An application of text analytics to fine-dining restaurant online reviews [Doctoral thesis, The Hong Kong Polytechnic University]. PolyU Institutional Research Archive. http://hdl.handle.net/10397/84023
33. Jacovi, A. ve Goldberg, Y. (2020). Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness? D. Jurafsky, J. Chai, N. Schluter ve J. Tetreault (Ed.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics içinde (s. 4198–4205). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.386
34. Jin, L. ve Hu, B. (2022). Influencing factors of online products decision-making oriented to tourism economy under the guidance of consumer psychology. Frontiers in Psychology, 13, Article 950754. https://doi.org/10.3389/fpsyg.2022.950754
35. Josiam, B. M., Kalldin, A. C. ve Duncan, J. L. (2015). Using the involvement construct to understand the motivations of customers of casual dining restaurants in the USA. Hospitality Review, 31(4), Article 9. https://digitalscholarship.fiu.edu/record/18342
36. Kiatkawsin, K. ve Han, H. (2019). What drives customers’ willingness to pay price premiums for luxury gastronomic experiences at Michelin-starred restaurants? International Journal of Hospitality Management, 82, 209–219. https://doi.org/10.1016/j.ijhm.2019.04.024
37. Kiatkawsin, K. ve Sutherland, I. (2020). Examining luxury restaurant dining experience towards sustainable reputation of the Michelin restaurant guide. Sustainability, 12(5), Article 2134. https://doi.org/10.3390/su12052134
38. Kim, J. H., Kim, S. ve Wang, L. (2025a). The role of sensory stimuli in delivering memorable dining experiences. International Journal of Contemporary Hospitality Management, 37(3), 825–852. https://doi.org/10.1108/IJCHM-05-2024-0696
39. Kim, S., Riswanto, A. L., Sherpa, S. ve Kim, H.-S. (2025b). Generation Z's Michelin choices: Survey and big data insights into Thailand's Michelin-starred restaurants. International Journal of Tourism Research, 27, e70135. https://doi.org/10.1002/jtr.70135
40. Kozinets, R. V. (2015). Netnography: Redefined (2. Baskı). Sage.
41. Landis, J. R. ve Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310
42. Lau, K. C., Lee, S. ve Phau, I. (2026). Michelin matters? examining perceived value, skepticism, and dining ıntentions. Journal of International Consumer Marketing, 38(1), 1–15. https://doi.org/10.1080/08961530.2025.2491077
43. Le, T. H., Arcodia, C., Novais, M. A., Kralj, A. ve Phan, T. C. (2021). Exploring the multi-dimensionality of authenticity in dining experiences using online reviews. Tourism Management, 85, Article 104292. https://doi.org/10.1016/j.tourman.2021.104292
44. Lee, H.-J. ve Ko, J.-Y. (2022). Culinary capital of Michelin restaurants consumers: A netnographic analysis. Journal of Hospitality and Tourism Management, 51, 415–423. https://doi.org/10.1016/j.jhtm.2022.04.011
45. Li, H., Yu, B. X. B., Li, G. ve Gao, H. (2023). Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews. Tourism Management, 96, Article 104707. https://doi.org/10.1016/j.tourman.2022.104707
46. Li, L., Goh, T.-T. ve Jin, D. (2020). How textual quality of online reviews affect classification performance: A case of deep learning sentiment analysis. Neural Computing and Applications, 32, 4387–4415. https://doi.org/10.1007/s00521-018-3865-7
47. Li, Z.-X., Liu, S.-F., Zhang, Y., Yin, Y., Ye, M. ve Xi, Y.-C. (2026). Will you purchase Michelin-starred prepared dishes? A mixed-methods analysis of the effect of the fine-dining democratization on consumer behavior. Current Psychology, 45(1), Article 62. https://doi.org/10.1007/s12144-025-08881-x
48. Lin, M.-P., Marine-Roig, E. ve Llonch-Molina, N. (2022). Gastronomy tourism and well-being: Evidence from Taiwan and Catalonia Michelin-starred restaurants. International Journal of Environmental Research and Public Health, 19(5), Article 2778. https://doi.org/10.3390/ijerph19052778
49. Liu, C.-R., Wang, Y.-C., Kuo, T.-M., Chen, H. ve Tsui, C.-H. (2022). Memorable dining experiences with five senses: Conceptualization and scale development. Journal of Hospitality and Tourism Management, 53, 198–207. https://doi.org/10.1016/j.jhtm.2022.11.001
50. Mathayomchan, B. ve Taecharungroj, V. (2020). “How was your meal?” Examining customer experience using Google Maps reviews. International Journal of Hospitality Management, 90, Article 102641. https://doi.org/10.1016/j.ijhm.2020.102641
51. Maulana, I., Nurwati, E. ve Nugroho, S. D. (2025). Creating a memorable dining experience: The influence of taste, ambience, and luxury perception through brand image. Asian Journal of Management, Entrepreneurship and Social Science, 5(3), 1303–1328.
52. Mehrabian, A. ve Russell, J. A. (1974). An approach to environmental psychology. The MIT Press.
53. Mehraliyev, F., Chan, I. C. C. ve Kirilenko, A. P. (2022). Sentiment analysis in hospitality and tourism: A thematic and methodological review. International Journal of Contemporary Hospitality Management, 34(1), 46–77. https://doi.org/10.1108/IJCHM-02-2021-0132
54. Merkl, M., Horvath, D. ve Cosovan, A. (2026). Experience-oriented food design in a restaurant context: A study of guest reflections on a fine dining starter. Journal of Food Technology Research, 13(1), 11–21. https://doi.org/10.18488/jftr.v13i1.4714
55. Mhlanga, O. (2015). Electronic meal experience: a gap analysis of online Cape Town restaurant comments. African Journal of Hospitality, Tourism and Leisure, 4(1), 1–11.
56. Michelin Guide. (2024). The inspection process. https://guide.michelin.com/sg/en/the-inspection-process-sg
57. Mohammad, S. M. ve Turney, P. D. (2013). Crowdsourcing a word–emotion association lexicon. Computational Intelligence, 29(3), 436–465. https://doi.org/10.1111/j.1467-8640.2012.00460.x
58. Mohammad, S. M., Salameh, M. ve Kiritchenko, S. (2016). Sentiment lexicons for Arabic social media. N. Calzolari, K. Choukri, T. Declerck, S. Goggi, M. Grobelnik, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk ve S. Piperidis (Ed.), Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) içinde (s. 33–37). European Language Resources Association.
59. Montargot, N., Kallmuenzer, A. ve Kraus, S. (2022). Haute cuisine three-star restaurants’ representation on websites and dining guides: A lexicometric analysis. International Journal of Contemporary Hospitality Management, 34(9), 3376–3393. https://doi.org/10.1108/IJCHM-07-2021-0851
60. Norris, C. L., Russen, M. ve Taylor, S., Jr. (2023). Expanding the experiential value scale to predict independent restaurant dining intent. Journal of Hospitality and Tourism Insights, 6(2), 613–631. https://doi.org/10.1108/JHTI-09-2021-0252
61. Özdemir, B. (2010). Dışarıda yemek yeme olgusu: Kuramsal bir model önerisi. Anatolia: Turizm Araştırmaları Dergisi, 21(2), 218–232.
62. Özdemir, B. (2018). Yemek deneyimi. K. Karamustafa (Ed.), Yiyecek ve içecek yönetimi içinde (s. 47–74). Detay Yayıncılık.
63. Pang, B. ve Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/1500000011
64. Pantelidis, I. S. (2010). Electronic meal experience: A content analysis of online restaurant comments. Cornell Hospitality Quarterly, 51(4), 483–491. https://doi.org/10.1177/1938965510378574
65. Pookaiyaudom, G. (2026). Pleasure on a plate: Thai fine dining as a new form of recreation. International Journal of Gastronomy and Food Science, 44, Article 101480. https://doi.org/10.1016/j.ijgfs.2026.101480
66. Rattanaparinyanon, H., Vuthisopon, S., Saengnoree, A. ve Siripongdee, S. (2024). Drivers of revisit intentions at Michelin-starred restaurants in Thailand: The role of innovation, quality, and satisfaction. Pakistan Journal of Life and Social Sciences, 22(2), 12163–12181. https://doi.org/10.57239/PJLSS-2024-22.2.00870
67. Ribeiro, M. T., Singh, S. ve Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. J. DeNero, M. Finlayson ve S. Reddy (Ed.), Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations içinde (s. 97–101). Association for Computational Linguistics. https://doi.org/10.18653/v1/N16-3020
68. Rita, P., Vong, C., Pinheiro, F. ve Mimoso, J. (2023). A sentiment analysis of Michelin-starred restaurants. European Journal of Management and Business Economics, 32(3), 276–295. https://doi.org/10.1108/EJMBE-11-2021-0295
69. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
70. Ryu, K. (2005). DINESCAPE, emotions, and behavioral intentions in upscale restaurants [Doctoral dissertation, Kansas State University]. K-State Research Exchange. http://hdl.handle.net/2097/71
71. Saydam, M. B. ve Altun, Ö. (2023). An analysis of British Michelin-starred restaurants: Guests’ online reviews. British Food Journal, 125(11), 4214–4228. https://doi.org/10.1108/BFJ-03-2023-0236
72. Souki, G. Q., Oliveira, A. S., Guerreiro, M. M. M., Mendes, J. C. ve Moura, L. R. C. (2023). Do memorable restaurant experiences affect eWOM? The moderating effect of consumers’ behavioural engagement on social networking sites. The TQM Journal, 35(8), 2255–2281. https://doi.org/10.1108/TQM-06-2022-0200
73. Sparks, B. A., So, K. K. F. ve Bradley, G. L. (2016). Responding to negative online reviews: The effects of hotel responses on customer inferences of trust and concern. Tourism Management, 53, 74–85. https://doi.org/10.1016/j.tourman.2015.09.011
74. Stone, M. J., Soulard, J., Migacz, S. ve Wolf, E. (2018). Elements of memorable food, drink, and culinary tourism experiences. Journal of Travel Research, 57(8), 1121–1132. https://doi.org/10.1177/0047287517729758
75. Sun, C., Qiu, X., Xu, Y. ve Huang, X. (2019). How to fine-tune BERT for text classification? M. Sun, X. Huang, H. Ji, Z. Liu ve Y. Liu (Ed.), Chinese computational linguistics: 18th China National Conference, CCL 2019, Kunming, China, October 18–20, 2019, Proceedings içinde (s. 194–206). Springer. https://doi.org/10.1007/978-3-030-32381-3_16
76. Şahin, A., Çolakoğlu, Ü. ve Özdoğan, O. N. (2021). A research on customer experiences and perceptions for Michelin starred restaurants. Journal of Multidisciplinary Academic Tourism, 6(1), 61–72. https://doi.org/10.31822/jomat.835486
77. Taboada, M., Brooke, J., Tofiloski, M., Voll, K. ve Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307. https://doi.org/10.1162/COLI_a_00049
78. Thelwall, M., Buckley, K. ve Paltoglou, G. (2012). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163–173. https://doi.org/10.1002/asi.21662
79. Tian, G., Lu, L. ve McIntosh, C. (2021). What factors affect consumers’ dining sentiments and their ratings: Evidence from restaurant online review data. Food Quality and Preference, 88, Article 104060. https://doi.org/10.1016/j.foodqual.2020.104060
80. Tsaur, S. H. ve Lo, P. C. (2020). Measuring memorable dining experiences and related emotions in fine dining restaurants. Journal of Hospitality Marketing and Management, 29(8), 887–910. https://doi.org/10.1080/19368623.2020.1748157
81. Uzut, İ. (2025). Michelin yıldızlı restoranlara yönelik e-şikâyetlerin analizi: Türkiye örneği. Journal of Tourism and Gastronomy Studies, 13(3), 2611–2633. https://doi.org/10.21325/jotags.2025.1680
82. Vargas-Sanchez, A. ve López-Guzmán, T. (2022). Creative Process and Culinary Experiences in Michelin-Starred Restaurants. Journal of Culinary Science and Technology, 20(2), 97–116. https://doi.org/10.1080/15428052.2020.1808137
83. Vásquez, C. ve Chik, A. (2015). “I Am Not a Foodie…”: Culinary Capital in Online Reviews of Michelin Restaurants. Food and Foodways, 23(4), 231–250. https://doi.org/10.1080/07409710.2015.1102483
84. Vigneron, F. ve Johnson, L. W. (2017). Measuring perceptions of brand luxury. F. Morhart, K. Wilcox ve S. Czellar (Ed.), Advances in luxury brand management içinde (s. 199–234). Palgrave Macmillan. https://doi.org/10.1007/978-3-319-51127-6_10
85. Wu, J.-J. ve Chang, S.-T. (2020). Exploring customer sentiment regarding online retail services: A topic-based approach. Journal of Retailing and Consumer Services, 55, Article 102145. https://doi.org/10.1016/j.jretconser.2020.102145
86. Xiang, Z., Du, Q., Ma, Y. ve Fan, W. (2018). Assessing reliability of social media data: Lessons from mining TripAdvisor hotel reviews. Information Technology and Tourism, 18(1–4), 43–59. https://doi.org/10.1007/s40558-017-0098-z
87. Yılmaz, S., Güzel, F. Ö. ve Arman, A. (2025). Lüks restoranlarda müşteri deneyimini şekillendiren unsurların çevrimiçi yorumlara yansıması: Antalya 7 Mehmet Restoranı örneği. Journal of Gastronomy, Hospitality and Travel, 8(2), 436–450. https://doi.org/10.33083/joghat.2025.504
88. Zhang, L., Wang, S. ve Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), Article e1253. https://doi.org/10.1002/widm.1253