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王雅琦
高玉婷
國際旅遊與管理學院
國際酒店管理碩士學位課程(英文學制)
碩士
Leveraging Big Data Analytics to Uncover New Insights into Customer Intent to Revisit Hotels
Leveraging Big Data Analytics to Uncover New Insights into Customer Intent to Revisit Hotels
顧客重訪酒店 ; 大數據 ; 市場營銷 ; 酒店評論抓取 ; 機器學習
Customer revisit hotels ; big data ; marketing ; hotel review crawling ; Machine Learning
公開日期：23/5/2028
這項研究調查了影響酒店客人返回意願的因素，利用大資料分析和機器學習技術提供對客戶偏好的更深入瞭解。使用樸素貝葉斯分類方法，從37000條評論的資料集中識別出10001條明確表明退貨意圖的評論。通過詞頻分析和SPSS統計方法，提取了62個特徵類別，然後使用7Ps行銷下拉式列示方塊架對其進行系統分類，以確定影響客人重複訪問的關鍵因素。為了進一步細分客戶偏好，應用了K-means聚類演算法，揭示了十個具有獨特期望和優先順序的不同細分市場。
結果表明，酒店客房品質和服務體驗是決定退貨意願的最關鍵因素。其他重要因素包括餐飲品質、地理位置、周邊設施的便利性以及與酒店員工的互動。值得注意的是，免費升級、免費迎賓飲料和個性化服務等元素成為了重要的激勵因素，尤其是對於家庭旅行者來說，他們往往強調物有所值，而不是絕對定價。相比之下，商務旅行者和休閒遊客表現出不同的偏好，關注服務流程的效率、靠近商務或旅遊景點以及舒適設施等因素。
這項研究通過將大資料分析與消費者行為分析相結合，為酒店管理和行銷文獻做出了貢獻，為理解客戶保留率提供了一種新方法。雖然之前的研究已經確定了影響重複入住的因素，但這項研究更進一步，提供了一個定量和資料驅動的客戶細分框架。研究結果表明，酒店經理應優先考慮客房品質、前臺員工的服務培訓和戰略促銷活動，以提高回頭率。此外，該研究強調了定價感知的文化和地區意義，特別是在大灣區，客戶更傾向于根據感知價值而不是絕對成本來評估酒店。
儘管有貢獻，但該研究承認存在某些局限性。首先，它只關注文本評論，沒有納入客人的人口統計或行為資料，這可以更全面地瞭解客戶偏好。其次，該資料集僅限於大灣區的酒店，這可能會限制研究結果在其他地區的推廣。未來的研究可以擴大地理範圍，並整合情境情感分析，以進一步完善客人保留的預測模型。
通過提供酒店客人細分的資料驅動視角，本研究為酒店管理者提供了可操作的見解，旨在通過個性化服務、有針對性的促銷和體驗驅動的行銷策略來提高客戶滿意度並改善保留策略。
結果表明，酒店客房品質和服務體驗是決定退貨意願的最關鍵因素。其他重要因素包括餐飲品質、地理位置、周邊設施的便利性以及與酒店員工的互動。值得注意的是，免費升級、免費迎賓飲料和個性化服務等元素成為了重要的激勵因素，尤其是對於家庭旅行者來說，他們往往強調物有所值，而不是絕對定價。相比之下，商務旅行者和休閒遊客表現出不同的偏好，關注服務流程的效率、靠近商務或旅遊景點以及舒適設施等因素。
這項研究通過將大資料分析與消費者行為分析相結合，為酒店管理和行銷文獻做出了貢獻，為理解客戶保留率提供了一種新方法。雖然之前的研究已經確定了影響重複入住的因素，但這項研究更進一步，提供了一個定量和資料驅動的客戶細分框架。研究結果表明，酒店經理應優先考慮客房品質、前臺員工的服務培訓和戰略促銷活動，以提高回頭率。此外，該研究強調了定價感知的文化和地區意義，特別是在大灣區，客戶更傾向于根據感知價值而不是絕對成本來評估酒店。
儘管有貢獻，但該研究承認存在某些局限性。首先，它只關注文本評論，沒有納入客人的人口統計或行為資料，這可以更全面地瞭解客戶偏好。其次，該資料集僅限於大灣區的酒店，這可能會限制研究結果在其他地區的推廣。未來的研究可以擴大地理範圍，並整合情境情感分析，以進一步完善客人保留的預測模型。
通過提供酒店客人細分的資料驅動視角，本研究為酒店管理者提供了可操作的見解，旨在通過個性化服務、有針對性的促銷和體驗驅動的行銷策略來提高客戶滿意度並改善保留策略。
This study investigates the factors that influence hotel guests' willingness to return, leveraging big data analytics and machine learning techniques to provide deeper insights into customer preferences. Using the Naïve Bayes classification method, 10,001 reviews explicitly indicating return intentions were identified from a dataset of 37,000 reviews. Through term frequency analysis and SPSS statistical methods, 62 feature categories were extracted, which were then systematically categorized using the 7Ps marketing mix framework to determine key factors influencing guests’ repeat visits. To further segment customer preferences, a K-means clustering algorithm was applied, revealing ten distinct market segments with unique expectations and priorities.
The results indicate that hotel room quality and service experience are the most critical determinants of return intention. Other important factors include dining quality, geographic location, convenience of surrounding facilities, and interactions with hotel staff. Notably, elements such as free upgrades, complimentary welcome drinks, and personalized services emerged as significant motivators, particularly for family travelers, who tend to emphasize value for money over absolute pricing. In contrast, business travelers and leisure tourists display distinct preferences, focusing on factors such as efficiency in service processes, proximity to business or tourist attractions, and comfort-enhancing amenities.
This research contributes to the hotel management and marketing literature by integrating big data analytics with consumer behavior analysis, offering a novel approach to understanding customer retention. While previous studies have identified factors influencing repeat stays, this study goes further by providing a quantitative and data-driven customer segmentation framework. The findings suggest that hotel managers should prioritize room quality, service training for front-desk staff, and strategic promotional offerings to enhance return rates. Additionally, the study highlights the cultural and regional significance of pricing perception, particularly in the Greater Bay Area, where customers are more inclined to evaluate hotels based on perceived value rather than absolute cost.
Despite its contributions, the study acknowledges certain limitations. First, it focuses solely on textual reviews without incorporating demographic or behavioral data from guests, which could provide a more holistic understanding of customer preferences. Second, the dataset is limited to hotels within the Greater Bay Area, potentially restricting the generalizability of findings to other regions. Future research could expand the geographical scope and integrate contextual sentiment analysis to further refine predictive models for guest retention.
By providing a data-driven perspective on hotel guest segmentation, this study offers actionable insights for hotel managers aiming to enhance customer satisfaction and improve retention strategies through personalized service offerings, targeted promotions, and experience-driven marketing strategies.
The results indicate that hotel room quality and service experience are the most critical determinants of return intention. Other important factors include dining quality, geographic location, convenience of surrounding facilities, and interactions with hotel staff. Notably, elements such as free upgrades, complimentary welcome drinks, and personalized services emerged as significant motivators, particularly for family travelers, who tend to emphasize value for money over absolute pricing. In contrast, business travelers and leisure tourists display distinct preferences, focusing on factors such as efficiency in service processes, proximity to business or tourist attractions, and comfort-enhancing amenities.
This research contributes to the hotel management and marketing literature by integrating big data analytics with consumer behavior analysis, offering a novel approach to understanding customer retention. While previous studies have identified factors influencing repeat stays, this study goes further by providing a quantitative and data-driven customer segmentation framework. The findings suggest that hotel managers should prioritize room quality, service training for front-desk staff, and strategic promotional offerings to enhance return rates. Additionally, the study highlights the cultural and regional significance of pricing perception, particularly in the Greater Bay Area, where customers are more inclined to evaluate hotels based on perceived value rather than absolute cost.
Despite its contributions, the study acknowledges certain limitations. First, it focuses solely on textual reviews without incorporating demographic or behavioral data from guests, which could provide a more holistic understanding of customer preferences. Second, the dataset is limited to hotels within the Greater Bay Area, potentially restricting the generalizability of findings to other regions. Future research could expand the geographical scope and integrate contextual sentiment analysis to further refine predictive models for guest retention.
By providing a data-driven perspective on hotel guest segmentation, this study offers actionable insights for hotel managers aiming to enhance customer satisfaction and improve retention strategies through personalized service offerings, targeted promotions, and experience-driven marketing strategies.
2025
英文
109
Executive Abstract IV
List of Figures VII
List of Tables VIII
Chapter One Introduction 1
Chapter Two Literature Review 10
2.1 Digital Technologies, Big Data, and Automated Text Analysis in the Hotel Industry 10
2.2 Hotel Industry Feature Extraction and Customer Segmentation 14
2.3 Marketing Mix 18
Chapter Three Research Method 21
3.1 Data Collection 24
3.2 Comment Selection 29
3.3 Feature Extraction 34
3.4 K-means Algorithm Segmentation 40
3.4.1 Data Preparation and Preprocessing 42
3.4.2 Feature Extraction 43
3.4.3 Feature Fusion and Clustering 46
Chapter Four Results 50
4.1 Identification of Key Features 50
4.1.1 Marketing Mix Classification Results 52
4.1.2 Identifying Segmented User Preferences 56
Chapter Five Discussion and Conclusion 65
5.1 Conclusion 65
5.2 Theoretical Significance 66
5.3 Practical Implications 69
5.3.1 Product recommendations 69
5.3.2 Personnel perception characteristics 70
5.3.3 Guest role perspective 70
5.3.4 Price factors 71
5.3.5 Manager strategy recommendations 71
5.4 Limitations and Future Research 72
References 74
Curriculum Vitae 84
Appendices 86
List of Figures VII
List of Tables VIII
Chapter One Introduction 1
Chapter Two Literature Review 10
2.1 Digital Technologies, Big Data, and Automated Text Analysis in the Hotel Industry 10
2.2 Hotel Industry Feature Extraction and Customer Segmentation 14
2.3 Marketing Mix 18
Chapter Three Research Method 21
3.1 Data Collection 24
3.2 Comment Selection 29
3.3 Feature Extraction 34
3.4 K-means Algorithm Segmentation 40
3.4.1 Data Preparation and Preprocessing 42
3.4.2 Feature Extraction 43
3.4.3 Feature Fusion and Clustering 46
Chapter Four Results 50
4.1 Identification of Key Features 50
4.1.1 Marketing Mix Classification Results 52
4.1.2 Identifying Segmented User Preferences 56
Chapter Five Discussion and Conclusion 65
5.1 Conclusion 65
5.2 Theoretical Significance 66
5.3 Practical Implications 69
5.3.1 Product recommendations 69
5.3.2 Personnel perception characteristics 70
5.3.3 Guest role perspective 70
5.3.4 Price factors 71
5.3.5 Manager strategy recommendations 71
5.4 Limitations and Future Research 72
References 74
Curriculum Vitae 84
Appendices 86
1. Abdullah, D., Jayaraman, K., & Kamal, S. B. M. (2016). A conceptual model of interactive hotel website: The role of perceived website interactivity and customer perceived value toward website revisit intention. Procedia economics and finance, 37, 170-175.
2. Abdullah, O., Sufi, T., & Kumar, S. (2023). Impact of Food and Beverage Quality and Service Quality Towards Customer Satisfaction and Customer Retention, A Study of Five-Star Hotels. International Journal of Professional Business Review: Int. J. Prof. Bus. Rev., 8(5), 98.
3. AbuKhalifeh, A. N., & Som, A. P. M. (2012). Service quality management in hotel industry: a conceptual framework for food and beverage departments. International journal of business and management, 7(14), 135-141.
4. Agarwal, L., Thakral, K., Bhatt, G., & Mittal, A. (2019). Authorship clustering using tf-idf weighted word-embeddings. Proceedings of the 11th Annual Meeting of the Forum for Information Retrieval Evaluation,
5. Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International journal of hospitality management, 80, 52-77.
6. Ahmad, W., & Sun, J. (2018). Modeling consumer distrust of online hotel reviews. International journal of hospitality management, 71, 77-90.
7. Akın, H. B., & Şentürk, E. (2012). Bireylerin mutluluk düzeylerinin ordinal lojistik regresyon analizi ile incelenmesi-analysing levels of happiness of individuals with ordinal logistic analysis. Öneri dergisi, 10(37), 183-193.
8. Aksu, A., & Hiscott, R. (2022). Persistent Holocene outflow from the Black Sea to the eastern Mediterranean Sea still contradicts the Noah's Flood Hypothesis: A review of 1997–2021 evidence and a regional paleoceanographic synthesis for the latest Pleistocene–Holocene. Earth-Science Reviews, 227, 103960.
9. Al Zoubi, A. M. M. (2024). Spam Reviews Detection Models in Multilingual Contexts applying Sentiment Analysis, Metaheuristics, and Advanced Word Embedding.
10. Alaimo, C., Kallinikos, J., & Valderrama, E. (2020). Platforms as service ecosystems: Lessons from social media. Journal of information technology, 35(1), 25-48.
11. Amofah, O., Gyamfi, I., & Tutu, C. O. (2016). The influence of service marketing mix on customer choice of repeat purchase of restaurant in Kumasi, Ghana. European Journal of Business and Management, 8(11), 102-112.
12. Aras, M., Persada, I. N., & Nabella, S. D. (2023). The influence of service quality, Trust, and facilities on the decision to choose SP Hotel Batam. International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC), 1(4), 417-431.
13. Bagherzadeh, S., Shokouhyar, S., Jahani, H., & Sigala, M. (2021). A generalizable sentiment analysis method for creating a hotel dictionary: using big data on TripAdvisor hotel reviews. Journal of Hospitality and Tourism Technology, 12(2), 210-238.
14. Bohner, G., & Schlüter, L. E. (2014). A room with a viewpoint revisited: Descriptive norms and hotel guests' towel reuse behavior. PLoS One, 9(8), e104086.
15. Borden, N. H. (1964). The concept of the marketing mix. Journal of Advertising Research, 4(2), 2-7.
16. Bore, I., Rutherford, C., Glasgow, S., Taheri, B., & Antony, J. (2017). A systematic literature review on eWOM in the hotel industry: Current trends and suggestions for future research. Hospitality & Society, 7(1), 63-85.
17. Borghi, M., & Mariani, M. M. (2024). Asymmetrical influences of service robots’ perceived performance on overall customer satisfaction: An empirical investigation leveraging online reviews. Journal of Travel Research, 63(5), 1086-1111.
18. Camilleri, M. A., & Filieri, R. (2023). Customer satisfaction and loyalty with online consumer reviews: Factors affecting revisit intentions. International journal of hospitality management, 114, 103575.
19. Cantallops, A. S., & Salvi, F. (2014). New consumer behavior: A review of research on eWOM and hotels. International journal of hospitality management, 36, 41-51.
20. Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naïve Bayes algorithm. Knowledge-Based Systems, 192, 105361.
21. Chen, Z., Cao, H., Wang, H., Xu, F., Kostakos, V., & Li, Y. (2020). Will you come back/check-in again? understanding characteristics leading to urban revisitation and re-check-in. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(3), 1-27.
22. Cheng, K. K., Lam, T. H., & Leung, C. C. (2022). Wearing face masks in the community during the COVID-19 pandemic: altruism and solidarity. The Lancet, 399(10336), e39-e40.
23. Chia, R., Sabir, M. S., Bandres-Ciga, S., Saez-Atienzar, S., Reynolds, R. H., Gustavsson, E., Walton, R. L., Ahmed, S., Viollet, C., & Ding, J. (2021). Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture. Nature genetics, 53(3), 294-303.
24. Choi, Y., Choi, M., Oh, M., & Kim, S. (2020). Service robots in hotels: understanding the service quality perceptions of human-robot interaction. Journal of Hospitality Marketing & Management, 29(6), 613-635.
25. Çınar, K. (2020). Customer based brand equity models in hotel industry: A literature review. Strategic Innovative Marketing and Tourism: 8th ICSIMAT, Northern Aegean, Greece, 2019, 281-288.
26. de Oliveira Gosson, G. A., Renda, A. I., Correia, M. B., & Antonio, N. (2022). Hotel customer segmentation and sentiment analysis through online reviews: an analysis of selected European markets. Tourism & Management Studies, 18(1), 29-40.
27. Del Vecchio, P., Mele, G., Ndou, V., & Secundo, G. (2018). Creating value from social big data: Implications for smart tourism destinations. Information processing & management, 54(5), 847-860.
28. Dickinger, A., & Mazanec, J. A. (2015). Significant word items in hotel guest reviews: A feature extraction approach. Tourism Recreation Research, 40(3), 353-363.
29. Do Nguyen, Q., & Nguyen, H. T. (2023). Entrepreneurship education and entrepreneurial intention: The mediating role of entrepreneurial capacity. The International Journal of Management Education, 21(1), 100730.
30. Dolnicar, S. (2022). Market segmentation for e-tourism. In Handbook of e-Tourism (pp. 849-863). Springer.
31. Dominici, G. (2009). From marketing mix to e-marketing mix: a literature overview and classification. International journal of business and management, 4(9), 17-24.
32. Dyshkantiuk, O., Salamatina, S., Polishchuk, L., Komarnytskyi, I., Tserklevych, V., & Nedobiichuk, T. (2020). Modern hotel business management tools. International Journal of Advanced Research in Engineering and Technology, 11(6).
33. Emir, O., & Kozak, M. (2011). Perceived importance of attributes on hotel guests' repeat visit intentions. Tourism: An International Interdisciplinary Journal, 59(2), 131-143.
34. Engle, R. F., Giglio, S., Kelly, B., Lee, H., & Stroebel, J. (2020). Hedging climate change news. The Review of Financial Studies, 33(3), 1184-1216.
35. Fauzan, A., Novianti, A., Ramadhani, R. R. M. A., & Adhiwibawa, M. A. S. (2022). Analysis of hotels spatial clustering in Bali: density-based spatial clustering of application noise (DBSCAN) algorithm approach. EKSAKTA: Journal of Sciences and Data Analysis, 25-38.
36. Filieri, R., Lin, Z., Pino, G., Alguezaui, S., & Inversini, A. (2021). The role of visual cues in eWOM on consumers’ behavioral intention and decisions. Journal of business research, 135, 663-675.
37. Fisk, R. P., Brown, S. W., & Bitner, M. J. (1993). Tracking the evolution of the services marketing literature. Journal of retailing, 69(1), 61-103.
38. Francesco, G., & Roberta, G. (2019). Cross-country analysis of perception and emphasis of hotel attributes. Tourism management, 74, 24-42.
39. Fuchs, M., & Höpken, W. (2022). Clustering: Hierarchical, k-Means, DBSCAN. In Applied Data Science in Tourism: Interdisciplinary Approaches, Methodologies, and Applications (pp. 129-149). Springer.
40. Gallagher, M., & Mansour, A. (2000). An analysis of hotel real estate market dynamics. Journal of Real Estate Research, 19(2), 133-164.
41. Garrido-Moreno, A., García-Morales, V. J., Lockett, N., & King, S. (2018). The missing link: Creating value with social media use in hotels. International journal of hospitality management, 75, 94-104.
42. Goi, C. L. (2009). A review of marketing mix: 4Ps or more. International journal of marketing studies, 1(1), 2-15.
43. Gregoriades, A., Pampaka, M., Herodotou, H., & Christodoulou, E. (2023). Explaining tourist revisit intention using natural language processing and classification techniques. Journal of Big Data, 10(1), 60.
44. Gupta, P., Gandhi, S., & Chakravarthi, B. R. (2021). Leveraging transfer learning techniques-bert, roberta, albert and distilbert for fake review detection. Proceedings of the 13th Annual Meeting of the Forum for Information Retrieval Evaluation,
45. Han, H., & Hyun, S. S. (2017). Impact of hotel-restaurant image and quality of physical-environment, service, and food on satisfaction and intention. International journal of hospitality management, 63, 82-92.
46. Huynh, G., Nguyen, T. V., Nguyen, D. D., Lam, Q. M., Pham, T. N., & Nguyen, H. T. N. (2021). Knowledge about COVID-19, beliefs and vaccination acceptance against COVID-19 among high-risk people in Ho Chi Minh City, Vietnam. Infection and drug resistance, 1773-1780.
47. Islam, M. S., Sarkar, T., Khan, S. H., Kamal, A.-H. M., Hasan, S. M., Kabir, A., Yeasmin, D., Islam, M. A., Chowdhury, K. I. A., & Anwar, K. S. (2020). COVID-19–related infodemic and its impact on public health: A global social media analysis. The American journal of tropical medicine and hygiene, 103(4), 1621.
48. Išoraitė, M. (2016). Marketing mix theoretical aspects. International Journal of Research-Granthaalayah, 4(6), 25-37.
49. Jardim, S., & Mora, C. (2022). Customer reviews sentiment-based analysis and clustering for market-oriented tourism services and products development or positioning. Procedia Computer Science, 196, 199-206.
50. Jing, X. (2017). Concept-level sentiment analysis of online hotel reviews Purdue University].
51. Kahn, M. E., & Liu, P. (2016). Utilizing “big data” to improve the hotel sector’s energy efficiency: Lessons from recent economics research. Cornell Hospitality Quarterly, 57(2), 202-210.
52. Karim, A. A., Ibrahim, M. A., Hosny, M. M., Ibrahim, A. H., Mohamed, R. A., Elbanbawy, M., Al-Zoghby, A. M., Foad, K., & Moawad, I. F. (2024). A Comparative Analysis of Arabic Text Summarization Techniques: Evaluating Word Frequency, K-Means Clustering, and PageRank Algorithm. 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC),
53. Kaya, K., Yılmaz, Y., Yaslan, Y., Öğüdücü, Ş. G., & Çıngı, F. (2022). Demand forecasting model using hotel clustering findings for hospitality industry. Information processing & management, 59(1), 102816.
54. Khoirunnisa, K., Wardani, D., Wijayanto, A., Cahyono, H. D., & Setiadi, H. (2024). Merchant segmentation using K-modes clustering as QRIS marketing recommendations. AIP Conference Proceedings,
55. Kim, T. T., Kim, W. G., & Kim, H.-B. (2009). The effects of perceived justice on recovery satisfaction, trust, word-of-mouth, and revisit intention in upscale hotels. Tourism management, 30(1), 51-62.
56. Kim, W. G., Li, J., Han, J. S., & Kim, Y. (2017). The influence of recent hotel amenities and green practices on guests’ price premium and revisit intention. Tourism economics, 23(3), 577-593.
57. Kim, Y.-J., & Kim, H.-S. (2022). The impact of hotel customer experience on customer satisfaction through online reviews. Sustainability, 14(2), 848.
58. Kirima, R. K., Makopondo, R., & Mutungi, M. (2017). Effect of external hotel design features on customer attraction and retention. International Academic Journal of Human Resource and Business Administration, 2(3), 282-289.
59. Ku, E. C., & Chen, C.-D. (2015). Cultivating travellers' revisit intention to e-tourism service: the moderating effect of website interactivity. Behaviour & Information Technology, 34(5), 465-478.
60. Kumar, M., Kumar, C., Kumar, N., & Kavitha, S. (2024). Efficient Hotel Rating Prediction from Reviews Using Ensemble Learning Technique. Wireless Personal Communications, 137(2), 1161-1187.
61. Lee, Y., Lee, S., & Kim, D.-Y. (2021). Exploring hotel guests' perceptions of using robot assistants. Tourism Management Perspectives, 37, 100781.
62. Li, S. (2020). The impact of service quality, self-service technology, and the corporate image on customer satisfaction and customer revisit intention among luxury hotels in Kuala Lumpur, Malaysia. International Journal of Services, Economics and Management, 11(1), 48-70.
63. Lim, Y.-S., Kim, J. S., Choi, J. H., Kim, J. M., & Shim, T. S. (2022). Solvatochromic discrimination of alcoholic solvents by structural colors of polydopamine nanoparticle thin films. Colloid and Interface Science Communications, 48, 100624.
64. Liu, J., Yu, Y., Mehraliyev, F., Hu, S., & Chen, J. (2022). What affects the online ratings of restaurant consumers: a research perspective on text-mining big data analysis. International Journal of Contemporary Hospitality Management, 34(10), 3607-3633.
65. Liu, S., Wang, X., Liu, M., & Zhu, J. (2017). Towards better analysis of machine learning models: A visual analytics perspective. Visual Informatics, 1(1), 48-56.
66. Liu, Y., Yang, Q., & Yuan, J. (2021). An empirical study on marketing strategy of Shenzhen coastal tourism resort based on SWOT and ROST CM6 text analysis. 2021 International Conference on Education, Information Management and Service Science (EIMSS),
67. Manoharan, A., & Singal, M. (2019). Organizational effectiveness in hospitality: Managers perspectives. International journal of hospitality management, 80, 123-125.
68. Marandi, A., Tasavori, M., & Najmi, M. (2024). New insights into hotel customer’s revisiting intentions, based on big data. International Journal of Contemporary Hospitality Management, 36(1), 292-311.
69. Marandi, V. Mitigation of Scale-Up Risks in Technology Transfer of Vaccines and other Bioproducts. Vaccine Research, 9-10.
70. Mariani, M., & Borghi, M. (2021). Customers’ evaluation of mechanical artificial intelligence in hospitality services: a study using online reviews analytics. International Journal of Contemporary Hospitality Management, 33(11), 3956-3976.
71. Masri, D., Apriyandi, A., & Harahap, B. (2022). Implementation of K-Means for Analysis of Factors Causing Consumer Satisfaction at Madani Hotel Medan City. Bulletin of Computer Science and Electrical Engineering, 3(2), 66-72.
72. McCarthy, E. J., Shapiro, S. J., & Perreault, W. D. (1979). Basic marketing. Irwin-Dorsey Ontario.
73. Mehraliyev, F., Chan, I. C. C., & 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.
74. Miklosik, A., & Evans, N. (2020). Impact of big data and machine learning on digital transformation in marketing: A literature review. Ieee Access, 8, 101284-101292.
75. Mishra, R. K., Raj, H., Urolagin, S., Jothi, J. A. A., & Nawaz, N. (2022). Cluster-based knowledge graph and entity-relation representation on tourism economical sentiments. Applied Sciences, 12(16), 8105.
76. Mohsin, A., Lengler, J., & Aguzzoli, R. (2015). Staff turnover in hotels: Exploring the quadratic and linear relationships. Tourism management, 51, 35-48.
77. Nadkarni, G. N., Lala, A., Bagiella, E., Chang, H. L., Moreno, P. R., Pujadas, E., Arvind, V., Bose, S., Charney, A. W., & Chen, M. D. (2020). Anticoagulation, bleeding, mortality, and pathology in hospitalized patients with COVID-19. Journal of the American College of Cardiology, 76(16), 1815-1826.
78. Nasim, Z., & Haider, S. (2022). Cluster analysis of urdu tweets. Journal of King Saud University-Computer and Information Sciences, 34(5), 2170-2179.
79. Nikjoo, A., & Bakhshi, H. (2019). The presence of tourists and residents in shared travel photos. Tourism management, 70, 89-98.
80. O'connor, P. (2010). Managing a hotel's image on TripAdvisor. Journal of Hospitality Marketing & Management, 19(7), 754-772.
81. Pan, J., & Liu, Q. (2024). Research on Innovative Improvement of Hotel Catering Service Quality Based on Multifactor Data Analysis. 2024 3rd International Conference on Humanities, Wisdom Education and Service Management (HWESM 2024),
82. Park, E., Kang, J., Choi, D., & Han, J. (2020). Understanding customers' hotel revisiting behaviour: a sentiment analysis of online feedback reviews. Current Issues in Tourism, 23(5), 605-611.
83. Park, S., Johnson, K. R., & Chaudhuri, S. (2019). Promoting work engagement in the hotel sector: review and analysis. Management Research Review, 42(8), 971-990.
84. Pasya, F. K., Islami, F. N., Hanifah, N., Zikrinawati, K., & Fahmy, Z. (2023). OTA (Online Travel Agent) Purchase Decision From Risk Perception With E-Trust As An Intervening: Keputusan Pembelian OTA (Online Travel Agent) Dari Persepsi Risiko Dengan E-Trust Sebagai Intervening. Journal of Multidisciplinary Science, 2(3), 160-169.
85. Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E., & Blair, M. E. (2017). Opening the black box: An open‐source release of Maxent. Ecography, 40(7), 887-893.
86. Raza, M. A., Siddiquei, A. N., Awan, H. M., & Bukhari, K. (2012). Relationship between service quality, perceived value, satisfaction and revisit intention in hotel industry. Interdisciplinary journal of contemporary research in business, 4(8), 788-805.
87. Ren, L., Qiu, H., Wang, P., & Lin, P. M. (2016). Exploring customer experience with budget hotels: Dimensionality and satisfaction. International journal of hospitality management, 52, 13-23.
88. Sanchez-Casado, N., Artal-Tur, A., & Tomaseti-Solano, E. (2019). Social media, customers' experience, and hotel loyalty programs. Tourism Analysis, 24(1), 27-41.
89. Sánchez-Franco, M. J., Navarro-García, A., & Rondán-Cataluña, F. J. (2019). A naive Bayes strategy for classifying customer satisfaction: A study based on online reviews of hospitality services. Journal of business research, 101, 499-506.
90. Saraswati, N. W. S., Bisena, I. K. A., Muku, I. D. M. K., & Krisna, G. G. E. (2023). Recognizing Hotel Visitors Preferences Based on Service Consumption Level Using K-Means Method. SINTECH (Science and Information Technology) Journal, 6(3), 173-181.
91. Scheuffelen, S., Kemper, J., & Brettel, M. (2019). How do human attitudes and values predict online marketing responsiveness? Comparing consumer segmentation bases toward brand purchase and marketing response. Journal of Advertising Research, 59(2), 142-157.
92. Shallan, M. S., Moawad, I. F., El Naggar, R., & Montasser, H. (2024). Using Machine Learning Techniques to Maximize Profitability in the Hospitality Industry. 2024 6th International Conference on Computing and Informatics (ICCI),
93. Sharipudin, M.-N. S., Cheung, M. L., De Oliveira, M. J., & Solyom, A. (2023). The role of post-stay evaluation on eWOM and hotel revisit intention among Gen Y. Journal of Hospitality & Tourism Research, 47(1), 57-83.
94. Siakalli, M., Masouras, A., & Papademetriou, C. (2017). e-Marketing in the hotel industry: marketing mix strategies. Strategic Innovative Marketing: 4th IC-SIM, Mykonos, Greece 2015,
95. Simarmata, B. M. P., Utomo, B., Astrini, F., Tio, I. C., & Susanto, P. (2022). Influence of CSR Activity, Service Quality and Brand Attitude to Customer’s Re-Visit Intention in Hotel Industry. International Journal of Islamic Education, Research and Multiculturalism (IJIERM), 4(1), 73-102.
96. Sobari, N., Usman, H., & Wathani, M. Z. (2017). The influence of Sharia compliance and marketing mix on satisfaction and intention of Muslim tourist to revisit Sharia hotel. International Conference on Business and Management Research (ICBMR 2017),
97. Sreenivas, G., Murthy, K. M., Gopali, K. P., Eedula, N., & Mamatha, H. (2023). Sentiment analysis of hotel reviews-a comparative study. 2023 IEEE 8th International Conference for Convergence in Technology (I2CT),
98. Subramaniam, J. R., Lyons, W. E., Liu, J., Bartnikas, T. B., Rothstein, J., Price, D. L., Cleveland, D. W., Gitlin, J. D., & Wong, P. C. (2002). Mutant SOD1 causes motor neuron disease independent of copper chaperone–mediated copper loading. Nature neuroscience, 5(4), 301-307.
99. Talekar, R. (2020). Classification of Customer Satisfaction for the Development of Hospitality Businesses Dublin, National College of Ireland].
100. Tianyang, W. (2021). A K-means group division and LSTM based method for hotel demand forecasting. Tech. Gaz, 28, 1345-1352.
101. Timofte, R., Agustsson, E., Van Gool, L., Yang, M.-H., & Zhang, L. (2017). Ntire 2017 challenge on single image super-resolution: Methods and results. Proceedings of the IEEE conference on computer vision and pattern recognition workshops,
102. Tran, X. V., McCullough, K., Blankenship, M., Barton, T., Cohen, S., Harris, T., Lopez, A., Simone, S., & Bolger, T. (2024). Impact of average daily rate on hotel’s newest atmosphere. International Hospitality Review.
103. Tsai, H., Gao, C., & Liu, H. (2024). Hotel performance in the Guangdong-Hong Kong-Macau greater bay area: A non-homogeneous perspective. Journal of Hospitality & Tourism Research, 10963480241245929.
104. Vujić, M., ĐorĎević, S., & Lakićević, M. (2019). Service quality and customer satisfaction in the hotel industry in Serbia. Менаџмент у хотелијерству и туризму, 7(1), 61-70.
105. Wan, J., Yao, L., & Tang, Q. (2022). Research on sentiment analysis of hotel reviews based on Word2vec method. Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022),
106. Wang, J., & Kim, Y. S. (2019). The Effect of Service Quality of Economy Hotels Through Mediating Customer Satisfaction on Re-visit Intention-Focused on Home Inn in China. Journal of Korean Society for Quality Management, 47(4), 875-894.
107. Wen, Z., Chen, Y., Liu, H., & Liang, Z. (2024). Text Mining Based Approach for Customer Sentiment and Product Competitiveness Using Composite Online Review Data. Journal of Theoretical and Applied Electronic Commerce Research, 19(3), 1776-1792.
108. Yadegaridehkordi, E., Foroughi, B., Iranmanesh, M., Nilashi, M., & Ghobakhloo, M. (2023). Determinants of environmental, financial, and social sustainable performance of manufacturing SMEs in Malaysia. Sustainable Production and Consumption, 35, 129-140.
109. Yadegaridehkordi, E., Nilashi, M., Shuib, L., Nasir, M. H. N. B. M., Asadi, S., Samad, S., & Awang, N. F. (2020). The impact of big data on firm performance in hotel industry. Electronic Commerce Research and Applications, 40, 100921.
110. Yang, Y., Lin, M. S., & Magnini, V. P. (2024). Do guests care more about hotel cleanliness during COVID-19? Understanding factors associated with cleanliness importance of hotel guests. International Journal of Contemporary Hospitality Management, 36(1), 239-258.
111. Yilmaz, Ö. D. (2018). Revisiting employee-guest interactions in hotels: An analysis of critical incidents. Journal of Tourism, Heritage & Services Marketing, 4(2), 17-23.
112. Yu, Y., Li, X., & Jai, T.-M. (2017). The impact of green experience on customer satisfaction: Evidence from TripAdvisor. International Journal of Contemporary Hospitality Management, 29(5), 1340-1361.
113. Zhang, K.-S., Chen, C.-M., & Chang, W.-Y. (2021). Customers’ willingness to import Hotel robots under the background of artificial intelligence based on SPSS data analysis. 2021 2nd International Conference on Artificial Intelligence and Education (ICAIE),
114. Zhang, X., & Yu, Q. (2017). Hotel reviews sentiment analysis based on word vector clustering. 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA),
115. Zhao, S., Lin, Q., Ran, J., Musa, S. S., Yang, G., Wang, W., Lou, Y., Gao, D., Yang, L., & He, D. (2020). Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. International journal of infectious diseases, 92, 214-217.
116. Zheng, Y. (2024). Research on Internet Product User Portrait Based on Kmeans Algorithm. Proceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control Engineering,
117. Zhong, J., & Lai, Q. (2024). Smart Agriculture System based on Internet of Things using Kernel K-Means with Support Vector Machine. 2024 Second International Conference on Data Science and Information System (ICDSIS),
2. Abdullah, O., Sufi, T., & Kumar, S. (2023). Impact of Food and Beverage Quality and Service Quality Towards Customer Satisfaction and Customer Retention, A Study of Five-Star Hotels. International Journal of Professional Business Review: Int. J. Prof. Bus. Rev., 8(5), 98.
3. AbuKhalifeh, A. N., & Som, A. P. M. (2012). Service quality management in hotel industry: a conceptual framework for food and beverage departments. International journal of business and management, 7(14), 135-141.
4. Agarwal, L., Thakral, K., Bhatt, G., & Mittal, A. (2019). Authorship clustering using tf-idf weighted word-embeddings. Proceedings of the 11th Annual Meeting of the Forum for Information Retrieval Evaluation,
5. Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International journal of hospitality management, 80, 52-77.
6. Ahmad, W., & Sun, J. (2018). Modeling consumer distrust of online hotel reviews. International journal of hospitality management, 71, 77-90.
7. Akın, H. B., & Şentürk, E. (2012). Bireylerin mutluluk düzeylerinin ordinal lojistik regresyon analizi ile incelenmesi-analysing levels of happiness of individuals with ordinal logistic analysis. Öneri dergisi, 10(37), 183-193.
8. Aksu, A., & Hiscott, R. (2022). Persistent Holocene outflow from the Black Sea to the eastern Mediterranean Sea still contradicts the Noah's Flood Hypothesis: A review of 1997–2021 evidence and a regional paleoceanographic synthesis for the latest Pleistocene–Holocene. Earth-Science Reviews, 227, 103960.
9. Al Zoubi, A. M. M. (2024). Spam Reviews Detection Models in Multilingual Contexts applying Sentiment Analysis, Metaheuristics, and Advanced Word Embedding.
10. Alaimo, C., Kallinikos, J., & Valderrama, E. (2020). Platforms as service ecosystems: Lessons from social media. Journal of information technology, 35(1), 25-48.
11. Amofah, O., Gyamfi, I., & Tutu, C. O. (2016). The influence of service marketing mix on customer choice of repeat purchase of restaurant in Kumasi, Ghana. European Journal of Business and Management, 8(11), 102-112.
12. Aras, M., Persada, I. N., & Nabella, S. D. (2023). The influence of service quality, Trust, and facilities on the decision to choose SP Hotel Batam. International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC), 1(4), 417-431.
13. Bagherzadeh, S., Shokouhyar, S., Jahani, H., & Sigala, M. (2021). A generalizable sentiment analysis method for creating a hotel dictionary: using big data on TripAdvisor hotel reviews. Journal of Hospitality and Tourism Technology, 12(2), 210-238.
14. Bohner, G., & Schlüter, L. E. (2014). A room with a viewpoint revisited: Descriptive norms and hotel guests' towel reuse behavior. PLoS One, 9(8), e104086.
15. Borden, N. H. (1964). The concept of the marketing mix. Journal of Advertising Research, 4(2), 2-7.
16. Bore, I., Rutherford, C., Glasgow, S., Taheri, B., & Antony, J. (2017). A systematic literature review on eWOM in the hotel industry: Current trends and suggestions for future research. Hospitality & Society, 7(1), 63-85.
17. Borghi, M., & Mariani, M. M. (2024). Asymmetrical influences of service robots’ perceived performance on overall customer satisfaction: An empirical investigation leveraging online reviews. Journal of Travel Research, 63(5), 1086-1111.
18. Camilleri, M. A., & Filieri, R. (2023). Customer satisfaction and loyalty with online consumer reviews: Factors affecting revisit intentions. International journal of hospitality management, 114, 103575.
19. Cantallops, A. S., & Salvi, F. (2014). New consumer behavior: A review of research on eWOM and hotels. International journal of hospitality management, 36, 41-51.
20. Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective naïve Bayes algorithm. Knowledge-Based Systems, 192, 105361.
21. Chen, Z., Cao, H., Wang, H., Xu, F., Kostakos, V., & Li, Y. (2020). Will you come back/check-in again? understanding characteristics leading to urban revisitation and re-check-in. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(3), 1-27.
22. Cheng, K. K., Lam, T. H., & Leung, C. C. (2022). Wearing face masks in the community during the COVID-19 pandemic: altruism and solidarity. The Lancet, 399(10336), e39-e40.
23. Chia, R., Sabir, M. S., Bandres-Ciga, S., Saez-Atienzar, S., Reynolds, R. H., Gustavsson, E., Walton, R. L., Ahmed, S., Viollet, C., & Ding, J. (2021). Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture. Nature genetics, 53(3), 294-303.
24. Choi, Y., Choi, M., Oh, M., & Kim, S. (2020). Service robots in hotels: understanding the service quality perceptions of human-robot interaction. Journal of Hospitality Marketing & Management, 29(6), 613-635.
25. Çınar, K. (2020). Customer based brand equity models in hotel industry: A literature review. Strategic Innovative Marketing and Tourism: 8th ICSIMAT, Northern Aegean, Greece, 2019, 281-288.
26. de Oliveira Gosson, G. A., Renda, A. I., Correia, M. B., & Antonio, N. (2022). Hotel customer segmentation and sentiment analysis through online reviews: an analysis of selected European markets. Tourism & Management Studies, 18(1), 29-40.
27. Del Vecchio, P., Mele, G., Ndou, V., & Secundo, G. (2018). Creating value from social big data: Implications for smart tourism destinations. Information processing & management, 54(5), 847-860.
28. Dickinger, A., & Mazanec, J. A. (2015). Significant word items in hotel guest reviews: A feature extraction approach. Tourism Recreation Research, 40(3), 353-363.
29. Do Nguyen, Q., & Nguyen, H. T. (2023). Entrepreneurship education and entrepreneurial intention: The mediating role of entrepreneurial capacity. The International Journal of Management Education, 21(1), 100730.
30. Dolnicar, S. (2022). Market segmentation for e-tourism. In Handbook of e-Tourism (pp. 849-863). Springer.
31. Dominici, G. (2009). From marketing mix to e-marketing mix: a literature overview and classification. International journal of business and management, 4(9), 17-24.
32. Dyshkantiuk, O., Salamatina, S., Polishchuk, L., Komarnytskyi, I., Tserklevych, V., & Nedobiichuk, T. (2020). Modern hotel business management tools. International Journal of Advanced Research in Engineering and Technology, 11(6).
33. Emir, O., & Kozak, M. (2011). Perceived importance of attributes on hotel guests' repeat visit intentions. Tourism: An International Interdisciplinary Journal, 59(2), 131-143.
34. Engle, R. F., Giglio, S., Kelly, B., Lee, H., & Stroebel, J. (2020). Hedging climate change news. The Review of Financial Studies, 33(3), 1184-1216.
35. Fauzan, A., Novianti, A., Ramadhani, R. R. M. A., & Adhiwibawa, M. A. S. (2022). Analysis of hotels spatial clustering in Bali: density-based spatial clustering of application noise (DBSCAN) algorithm approach. EKSAKTA: Journal of Sciences and Data Analysis, 25-38.
36. Filieri, R., Lin, Z., Pino, G., Alguezaui, S., & Inversini, A. (2021). The role of visual cues in eWOM on consumers’ behavioral intention and decisions. Journal of business research, 135, 663-675.
37. Fisk, R. P., Brown, S. W., & Bitner, M. J. (1993). Tracking the evolution of the services marketing literature. Journal of retailing, 69(1), 61-103.
38. Francesco, G., & Roberta, G. (2019). Cross-country analysis of perception and emphasis of hotel attributes. Tourism management, 74, 24-42.
39. Fuchs, M., & Höpken, W. (2022). Clustering: Hierarchical, k-Means, DBSCAN. In Applied Data Science in Tourism: Interdisciplinary Approaches, Methodologies, and Applications (pp. 129-149). Springer.
40. Gallagher, M., & Mansour, A. (2000). An analysis of hotel real estate market dynamics. Journal of Real Estate Research, 19(2), 133-164.
41. Garrido-Moreno, A., García-Morales, V. J., Lockett, N., & King, S. (2018). The missing link: Creating value with social media use in hotels. International journal of hospitality management, 75, 94-104.
42. Goi, C. L. (2009). A review of marketing mix: 4Ps or more. International journal of marketing studies, 1(1), 2-15.
43. Gregoriades, A., Pampaka, M., Herodotou, H., & Christodoulou, E. (2023). Explaining tourist revisit intention using natural language processing and classification techniques. Journal of Big Data, 10(1), 60.
44. Gupta, P., Gandhi, S., & Chakravarthi, B. R. (2021). Leveraging transfer learning techniques-bert, roberta, albert and distilbert for fake review detection. Proceedings of the 13th Annual Meeting of the Forum for Information Retrieval Evaluation,
45. Han, H., & Hyun, S. S. (2017). Impact of hotel-restaurant image and quality of physical-environment, service, and food on satisfaction and intention. International journal of hospitality management, 63, 82-92.
46. Huynh, G., Nguyen, T. V., Nguyen, D. D., Lam, Q. M., Pham, T. N., & Nguyen, H. T. N. (2021). Knowledge about COVID-19, beliefs and vaccination acceptance against COVID-19 among high-risk people in Ho Chi Minh City, Vietnam. Infection and drug resistance, 1773-1780.
47. Islam, M. S., Sarkar, T., Khan, S. H., Kamal, A.-H. M., Hasan, S. M., Kabir, A., Yeasmin, D., Islam, M. A., Chowdhury, K. I. A., & Anwar, K. S. (2020). COVID-19–related infodemic and its impact on public health: A global social media analysis. The American journal of tropical medicine and hygiene, 103(4), 1621.
48. Išoraitė, M. (2016). Marketing mix theoretical aspects. International Journal of Research-Granthaalayah, 4(6), 25-37.
49. Jardim, S., & Mora, C. (2022). Customer reviews sentiment-based analysis and clustering for market-oriented tourism services and products development or positioning. Procedia Computer Science, 196, 199-206.
50. Jing, X. (2017). Concept-level sentiment analysis of online hotel reviews Purdue University].
51. Kahn, M. E., & Liu, P. (2016). Utilizing “big data” to improve the hotel sector’s energy efficiency: Lessons from recent economics research. Cornell Hospitality Quarterly, 57(2), 202-210.
52. Karim, A. A., Ibrahim, M. A., Hosny, M. M., Ibrahim, A. H., Mohamed, R. A., Elbanbawy, M., Al-Zoghby, A. M., Foad, K., & Moawad, I. F. (2024). A Comparative Analysis of Arabic Text Summarization Techniques: Evaluating Word Frequency, K-Means Clustering, and PageRank Algorithm. 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC),
53. Kaya, K., Yılmaz, Y., Yaslan, Y., Öğüdücü, Ş. G., & Çıngı, F. (2022). Demand forecasting model using hotel clustering findings for hospitality industry. Information processing & management, 59(1), 102816.
54. Khoirunnisa, K., Wardani, D., Wijayanto, A., Cahyono, H. D., & Setiadi, H. (2024). Merchant segmentation using K-modes clustering as QRIS marketing recommendations. AIP Conference Proceedings,
55. Kim, T. T., Kim, W. G., & Kim, H.-B. (2009). The effects of perceived justice on recovery satisfaction, trust, word-of-mouth, and revisit intention in upscale hotels. Tourism management, 30(1), 51-62.
56. Kim, W. G., Li, J., Han, J. S., & Kim, Y. (2017). The influence of recent hotel amenities and green practices on guests’ price premium and revisit intention. Tourism economics, 23(3), 577-593.
57. Kim, Y.-J., & Kim, H.-S. (2022). The impact of hotel customer experience on customer satisfaction through online reviews. Sustainability, 14(2), 848.
58. Kirima, R. K., Makopondo, R., & Mutungi, M. (2017). Effect of external hotel design features on customer attraction and retention. International Academic Journal of Human Resource and Business Administration, 2(3), 282-289.
59. Ku, E. C., & Chen, C.-D. (2015). Cultivating travellers' revisit intention to e-tourism service: the moderating effect of website interactivity. Behaviour & Information Technology, 34(5), 465-478.
60. Kumar, M., Kumar, C., Kumar, N., & Kavitha, S. (2024). Efficient Hotel Rating Prediction from Reviews Using Ensemble Learning Technique. Wireless Personal Communications, 137(2), 1161-1187.
61. Lee, Y., Lee, S., & Kim, D.-Y. (2021). Exploring hotel guests' perceptions of using robot assistants. Tourism Management Perspectives, 37, 100781.
62. Li, S. (2020). The impact of service quality, self-service technology, and the corporate image on customer satisfaction and customer revisit intention among luxury hotels in Kuala Lumpur, Malaysia. International Journal of Services, Economics and Management, 11(1), 48-70.
63. Lim, Y.-S., Kim, J. S., Choi, J. H., Kim, J. M., & Shim, T. S. (2022). Solvatochromic discrimination of alcoholic solvents by structural colors of polydopamine nanoparticle thin films. Colloid and Interface Science Communications, 48, 100624.
64. Liu, J., Yu, Y., Mehraliyev, F., Hu, S., & Chen, J. (2022). What affects the online ratings of restaurant consumers: a research perspective on text-mining big data analysis. International Journal of Contemporary Hospitality Management, 34(10), 3607-3633.
65. Liu, S., Wang, X., Liu, M., & Zhu, J. (2017). Towards better analysis of machine learning models: A visual analytics perspective. Visual Informatics, 1(1), 48-56.
66. Liu, Y., Yang, Q., & Yuan, J. (2021). An empirical study on marketing strategy of Shenzhen coastal tourism resort based on SWOT and ROST CM6 text analysis. 2021 International Conference on Education, Information Management and Service Science (EIMSS),
67. Manoharan, A., & Singal, M. (2019). Organizational effectiveness in hospitality: Managers perspectives. International journal of hospitality management, 80, 123-125.
68. Marandi, A., Tasavori, M., & Najmi, M. (2024). New insights into hotel customer’s revisiting intentions, based on big data. International Journal of Contemporary Hospitality Management, 36(1), 292-311.
69. Marandi, V. Mitigation of Scale-Up Risks in Technology Transfer of Vaccines and other Bioproducts. Vaccine Research, 9-10.
70. Mariani, M., & Borghi, M. (2021). Customers’ evaluation of mechanical artificial intelligence in hospitality services: a study using online reviews analytics. International Journal of Contemporary Hospitality Management, 33(11), 3956-3976.
71. Masri, D., Apriyandi, A., & Harahap, B. (2022). Implementation of K-Means for Analysis of Factors Causing Consumer Satisfaction at Madani Hotel Medan City. Bulletin of Computer Science and Electrical Engineering, 3(2), 66-72.
72. McCarthy, E. J., Shapiro, S. J., & Perreault, W. D. (1979). Basic marketing. Irwin-Dorsey Ontario.
73. Mehraliyev, F., Chan, I. C. C., & 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.
74. Miklosik, A., & Evans, N. (2020). Impact of big data and machine learning on digital transformation in marketing: A literature review. Ieee Access, 8, 101284-101292.
75. Mishra, R. K., Raj, H., Urolagin, S., Jothi, J. A. A., & Nawaz, N. (2022). Cluster-based knowledge graph and entity-relation representation on tourism economical sentiments. Applied Sciences, 12(16), 8105.
76. Mohsin, A., Lengler, J., & Aguzzoli, R. (2015). Staff turnover in hotels: Exploring the quadratic and linear relationships. Tourism management, 51, 35-48.
77. Nadkarni, G. N., Lala, A., Bagiella, E., Chang, H. L., Moreno, P. R., Pujadas, E., Arvind, V., Bose, S., Charney, A. W., & Chen, M. D. (2020). Anticoagulation, bleeding, mortality, and pathology in hospitalized patients with COVID-19. Journal of the American College of Cardiology, 76(16), 1815-1826.
78. Nasim, Z., & Haider, S. (2022). Cluster analysis of urdu tweets. Journal of King Saud University-Computer and Information Sciences, 34(5), 2170-2179.
79. Nikjoo, A., & Bakhshi, H. (2019). The presence of tourists and residents in shared travel photos. Tourism management, 70, 89-98.
80. O'connor, P. (2010). Managing a hotel's image on TripAdvisor. Journal of Hospitality Marketing & Management, 19(7), 754-772.
81. Pan, J., & Liu, Q. (2024). Research on Innovative Improvement of Hotel Catering Service Quality Based on Multifactor Data Analysis. 2024 3rd International Conference on Humanities, Wisdom Education and Service Management (HWESM 2024),
82. Park, E., Kang, J., Choi, D., & Han, J. (2020). Understanding customers' hotel revisiting behaviour: a sentiment analysis of online feedback reviews. Current Issues in Tourism, 23(5), 605-611.
83. Park, S., Johnson, K. R., & Chaudhuri, S. (2019). Promoting work engagement in the hotel sector: review and analysis. Management Research Review, 42(8), 971-990.
84. Pasya, F. K., Islami, F. N., Hanifah, N., Zikrinawati, K., & Fahmy, Z. (2023). OTA (Online Travel Agent) Purchase Decision From Risk Perception With E-Trust As An Intervening: Keputusan Pembelian OTA (Online Travel Agent) Dari Persepsi Risiko Dengan E-Trust Sebagai Intervening. Journal of Multidisciplinary Science, 2(3), 160-169.
85. Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E., & Blair, M. E. (2017). Opening the black box: An open‐source release of Maxent. Ecography, 40(7), 887-893.
86. Raza, M. A., Siddiquei, A. N., Awan, H. M., & Bukhari, K. (2012). Relationship between service quality, perceived value, satisfaction and revisit intention in hotel industry. Interdisciplinary journal of contemporary research in business, 4(8), 788-805.
87. Ren, L., Qiu, H., Wang, P., & Lin, P. M. (2016). Exploring customer experience with budget hotels: Dimensionality and satisfaction. International journal of hospitality management, 52, 13-23.
88. Sanchez-Casado, N., Artal-Tur, A., & Tomaseti-Solano, E. (2019). Social media, customers' experience, and hotel loyalty programs. Tourism Analysis, 24(1), 27-41.
89. Sánchez-Franco, M. J., Navarro-García, A., & Rondán-Cataluña, F. J. (2019). A naive Bayes strategy for classifying customer satisfaction: A study based on online reviews of hospitality services. Journal of business research, 101, 499-506.
90. Saraswati, N. W. S., Bisena, I. K. A., Muku, I. D. M. K., & Krisna, G. G. E. (2023). Recognizing Hotel Visitors Preferences Based on Service Consumption Level Using K-Means Method. SINTECH (Science and Information Technology) Journal, 6(3), 173-181.
91. Scheuffelen, S., Kemper, J., & Brettel, M. (2019). How do human attitudes and values predict online marketing responsiveness? Comparing consumer segmentation bases toward brand purchase and marketing response. Journal of Advertising Research, 59(2), 142-157.
92. Shallan, M. S., Moawad, I. F., El Naggar, R., & Montasser, H. (2024). Using Machine Learning Techniques to Maximize Profitability in the Hospitality Industry. 2024 6th International Conference on Computing and Informatics (ICCI),
93. Sharipudin, M.-N. S., Cheung, M. L., De Oliveira, M. J., & Solyom, A. (2023). The role of post-stay evaluation on eWOM and hotel revisit intention among Gen Y. Journal of Hospitality & Tourism Research, 47(1), 57-83.
94. Siakalli, M., Masouras, A., & Papademetriou, C. (2017). e-Marketing in the hotel industry: marketing mix strategies. Strategic Innovative Marketing: 4th IC-SIM, Mykonos, Greece 2015,
95. Simarmata, B. M. P., Utomo, B., Astrini, F., Tio, I. C., & Susanto, P. (2022). Influence of CSR Activity, Service Quality and Brand Attitude to Customer’s Re-Visit Intention in Hotel Industry. International Journal of Islamic Education, Research and Multiculturalism (IJIERM), 4(1), 73-102.
96. Sobari, N., Usman, H., & Wathani, M. Z. (2017). The influence of Sharia compliance and marketing mix on satisfaction and intention of Muslim tourist to revisit Sharia hotel. International Conference on Business and Management Research (ICBMR 2017),
97. Sreenivas, G., Murthy, K. M., Gopali, K. P., Eedula, N., & Mamatha, H. (2023). Sentiment analysis of hotel reviews-a comparative study. 2023 IEEE 8th International Conference for Convergence in Technology (I2CT),
98. Subramaniam, J. R., Lyons, W. E., Liu, J., Bartnikas, T. B., Rothstein, J., Price, D. L., Cleveland, D. W., Gitlin, J. D., & Wong, P. C. (2002). Mutant SOD1 causes motor neuron disease independent of copper chaperone–mediated copper loading. Nature neuroscience, 5(4), 301-307.
99. Talekar, R. (2020). Classification of Customer Satisfaction for the Development of Hospitality Businesses Dublin, National College of Ireland].
100. Tianyang, W. (2021). A K-means group division and LSTM based method for hotel demand forecasting. Tech. Gaz, 28, 1345-1352.
101. Timofte, R., Agustsson, E., Van Gool, L., Yang, M.-H., & Zhang, L. (2017). Ntire 2017 challenge on single image super-resolution: Methods and results. Proceedings of the IEEE conference on computer vision and pattern recognition workshops,
102. Tran, X. V., McCullough, K., Blankenship, M., Barton, T., Cohen, S., Harris, T., Lopez, A., Simone, S., & Bolger, T. (2024). Impact of average daily rate on hotel’s newest atmosphere. International Hospitality Review.
103. Tsai, H., Gao, C., & Liu, H. (2024). Hotel performance in the Guangdong-Hong Kong-Macau greater bay area: A non-homogeneous perspective. Journal of Hospitality & Tourism Research, 10963480241245929.
104. Vujić, M., ĐorĎević, S., & Lakićević, M. (2019). Service quality and customer satisfaction in the hotel industry in Serbia. Менаџмент у хотелијерству и туризму, 7(1), 61-70.
105. Wan, J., Yao, L., & Tang, Q. (2022). Research on sentiment analysis of hotel reviews based on Word2vec method. Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022),
106. Wang, J., & Kim, Y. S. (2019). The Effect of Service Quality of Economy Hotels Through Mediating Customer Satisfaction on Re-visit Intention-Focused on Home Inn in China. Journal of Korean Society for Quality Management, 47(4), 875-894.
107. Wen, Z., Chen, Y., Liu, H., & Liang, Z. (2024). Text Mining Based Approach for Customer Sentiment and Product Competitiveness Using Composite Online Review Data. Journal of Theoretical and Applied Electronic Commerce Research, 19(3), 1776-1792.
108. Yadegaridehkordi, E., Foroughi, B., Iranmanesh, M., Nilashi, M., & Ghobakhloo, M. (2023). Determinants of environmental, financial, and social sustainable performance of manufacturing SMEs in Malaysia. Sustainable Production and Consumption, 35, 129-140.
109. Yadegaridehkordi, E., Nilashi, M., Shuib, L., Nasir, M. H. N. B. M., Asadi, S., Samad, S., & Awang, N. F. (2020). The impact of big data on firm performance in hotel industry. Electronic Commerce Research and Applications, 40, 100921.
110. Yang, Y., Lin, M. S., & Magnini, V. P. (2024). Do guests care more about hotel cleanliness during COVID-19? Understanding factors associated with cleanliness importance of hotel guests. International Journal of Contemporary Hospitality Management, 36(1), 239-258.
111. Yilmaz, Ö. D. (2018). Revisiting employee-guest interactions in hotels: An analysis of critical incidents. Journal of Tourism, Heritage & Services Marketing, 4(2), 17-23.
112. Yu, Y., Li, X., & Jai, T.-M. (2017). The impact of green experience on customer satisfaction: Evidence from TripAdvisor. International Journal of Contemporary Hospitality Management, 29(5), 1340-1361.
113. Zhang, K.-S., Chen, C.-M., & Chang, W.-Y. (2021). Customers’ willingness to import Hotel robots under the background of artificial intelligence based on SPSS data analysis. 2021 2nd International Conference on Artificial Intelligence and Education (ICAIE),
114. Zhang, X., & Yu, Q. (2017). Hotel reviews sentiment analysis based on word vector clustering. 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA),
115. Zhao, S., Lin, Q., Ran, J., Musa, S. S., Yang, G., Wang, W., Lou, Y., Gao, D., Yang, L., & He, D. (2020). Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. International journal of infectious diseases, 92, 214-217.
116. Zheng, Y. (2024). Research on Internet Product User Portrait Based on Kmeans Algorithm. Proceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control Engineering,
117. Zhong, J., & Lai, Q. (2024). Smart Agriculture System based on Internet of Things using Kernel K-Means with Support Vector Machine. 2024 Second International Conference on Data Science and Information System (ICDSIS),
