Feedback analysis of service quality through data mining approach
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Abstract
This study explores the application of data mining techniques to analyze customer feedback for improving service quality at Tanjung Lesung Beach Hotel. Utilizing the Knowledge Discovery in Databases (KDD) framework, the research systematically collected, cleaned, and analyzed 1,239 customer reviews from the Agoda platform. Through a thorough data cleaning process, 642 verified reviews were identified as authentic, providing a robust dataset for in-depth analysis. Sentiment analysis was employed to extract key insights, revealing both positive aspects of the hotel experience, such as staff friendliness and beach amenities, as well as areas requiring improvement, particularly related to room conditions and breakfast offerings. Trend analysis indicated an upward trend in customer satisfaction from 2018 to 2023, with a minor decline observed in 2024, suggesting the need for ongoing service enhancements. The findings emphasize the critical role of leveraging verified customer feedback to inform strategic improvements, ultimately aiming to enhance guest satisfaction and maintain a competitive edge in the hospitality industry. This research contributes to the field by demonstrating how data-driven approaches can optimize service quality and align organizational strategies with evolving customer expectations
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How to Cite
Singgalen, Y. A. . (2024) “Feedback analysis of service quality through data mining approach”, Jurnal Mantik, 8(3), pp. 1484-1494. doi: 10.35335/mantik.v8i3.5761.
References
Aakash, A., & Gupta Aggarwal, A. (2022). Assessment of Hotel Performance and Guest Satisfaction through eWOM: Big Data for Better Insights. International Journal of Hospitality and Tourism Administration, 23(2), 317–346. https://doi.org/10.1080/15256480.2020.1746218
Ahmad, F., & Guzmán, F. (2021). Negative online reviews, brand equity and emotional contagion. European Journal of Marketing, 55(11), 2825–2870. https://doi.org/10.1108/EJM-10-2019-0820
Al-Turaiki, I., & Altwaijry, N. (2021). A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection. Big Data, 9(3), 233–252. https://doi.org/10.1089/big.2020.0263
Ameur, A., Hamdi, S., & Ben Yahia, S. (2023). Sentiment Analysis for Hotel Reviews: A Systematic Literature Review. ACM Computing Surveys, 56(2). https://doi.org/10.1145/3605152
Aurangzeb, K., Ayub, N., & Alhussein, M. (2021). Aspect Based Multi-Labeling Using SVM Based Ensembler. IEEE Access, 9, 26026–26040. https://doi.org/10.1109/ACCESS.2021.3055768
Benlahbib, A., & Nfaoui, E. H. (2020). Aggregating customer review attributes for online reputation generation. IEEE Access, 8, 96550–96564. https://doi.org/10.1109/ACCESS.2020.2996805
Brzezi?ska-Wójcik, T. (2022). Service Quality Evaluation in Four-Star Hotels in Lublin Using the Contextual Analysis Method. Annales Universitatis Mariae Curie-Sklodowska. Sectio B, 77, 149–176. https://doi.org/10.17951/B.2022.77.0.149-176
Dogan, A., & Birant, D. (2021). Machine learning and data mining in manufacturing. In Expert Systems with Applications (Vol. 166). https://doi.org/10.1016/j.eswa.2020.114060
Ho, R. C., Withanage, M. S., & Khong, K. W. (2020). Sentiment drivers of hotel customers: a hybrid approach using unstructured data from online reviews. Asia-Pacific Journal of Business Administration, 12(3–4), 237–250. https://doi.org/10.1108/APJBA-09-2019-0192
Jeong, M., Shin, H. H., Lee, M., & Lee, J. (2023). Assessing brand performance consistency from consumer-generated media: the US hotel industry. International Journal of Contemporary Hospitality Management, 35(6), 2056–2083. https://doi.org/10.1108/IJCHM-12-2021-1516
Lai, X., Wang, F., & Wang, X. (2021). Asymmetric relationship between customer sentiment and online hotel ratings: the moderating effects of review characteristics. International Journal of Contemporary Hospitality Management, 33(6), 2137–2156. https://doi.org/10.1108/IJCHM-07-2020-0708
Molina-Coronado, B., Mori, U., Mendiburu, A., & Miguel-Alonso, J. (2020). Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process. IEEE Transactions on Network and Service Management, 17(4), 2451–2479. https://doi.org/10.1109/TNSM.2020.3016246
Nguyen, H. T. T., & Nguyen, T. X. (2023). Understanding customer experience with Vietnamese hotels by analyzing online reviews. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02098-8
Peng, Y. C., Cheng, M. S., & Hsu, P. Y. (2020). Constructing an Integrated Classifiers for Identifying Authenticity and Sentiment Analysis- A Case of Hotel Reviews. In Proceedings - 2020 International Computer Symposium, ICS 2020 (pp. 319–324). https://doi.org/10.1109/ICS51289.2020.00070
Pham, D. H. (2020). A latent aspect mining framework from textual reviews. Indian Journal of Computer Science and Engineering, 11(4), 347–359. https://doi.org/10.21817/indjcse/2020/v11i4/201104209
Ponnusamy, V., Humayun, M., Jhanjhi, N. Z., Yichiet, A., & Almufareh, M. F. (2021). Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks. Computer Systems Science and Engineering, 40(3), 1199–1215. https://doi.org/10.32604/CSSE.2022.018518
Rahmaningrum, S. A., & Oktaviana, P. P. (2020). Sentiment classification of hotel service review on traveloka sites using naïve bayes classifier (NBC) and binary logistic regression. In Journal of Physics: Conference Series (Vol. 1490, Issue 1). https://doi.org/10.1088/1742-6596/1490/1/012065
Rita, P., Ramos, R., Borges-Tiago, M. T., & Rodrigues, D. (2022). Impact of the rating system on sentiment and tone of voice: A Booking.com and TripAdvisor comparison study. International Journal of Hospitality Management, 104(2), 1–12. https://doi.org/10.1016/j.ijhm.2022.103245
Sánchez-Franco, M. J., & Rey-Tienda, S. (2024). The role of user-generated content in tourism decision-making: an exemplary study of Andalusia, Spain. Management Decision, 62(7), 2292–2328. https://doi.org/10.1108/MD-06-2023-0966
Sann, R., & Lai, P. C. (2020). Understanding homophily of service failure within the hotel guest cycle: Applying NLP-aspect-based sentiment analysis to the hospitality industry. International Journal of Hospitality Management, 91. https://doi.org/10.1016/j.ijhm.2020.102678
Sann, R., & Lai, P. C. (2021). Do expectations towards Thai hospitality differ? The views of English vs Chinese speaking travelers. International Journal of Culture, Tourism, and Hospitality Research, 15(1), 43–58. https://doi.org/10.1108/IJCTHR-01-2020-0010
Sann, R., Lai, P. C., Liaw, S. Y., & Chen, C. T. (2022). Predicting Online Complaining Behavior in the Hospitality Industry: Application of Big Data Analytics to Online Reviews. Sustainability (Switzerland), 14(3). https://doi.org/10.3390/su14031800
Sharma, H., & Aggarwal, A. G. (2021). The influence of user generated content on hotel sales: an Indian perspective. Journal of Modelling in Management, 16(4), 1358–1375. https://doi.org/10.1108/JM2-07-2020-0201
Shu, Z., Torralba, M. H., Carrasco, R. A., & López, M. F. B. (2023). Assessing customer satisfaction of London luxury hotels with the AHP method and the SERVPERF scale: a case study of customer reviews on TripAdvisor. In Procedia Computer Science (Vol. 221, pp. 73–80). https://doi.org/10.1016/j.procs.2023.07.011
Tanr?sevdi, A., Öztürk, G., & Öztürk, A. C. (2022). A supervised data mining approach for predicting comment card ratings. International Journal of Contemporary Hospitality Management, 34(5), 1823–1853. https://doi.org/10.1108/IJCHM-05-2021-0675
Thu, H. N. T. (2020). Measuring guest satisfaction from online reviews: Envidence in Vietnam. Cogent Social Sciences, 6(1). https://doi.org/10.1080/23311886.2020.1801117
Zhao, N., Gao, H., Wen, X., & Li, H. (2021). Combination of convolutional neural network and gated recurrent unit for aspect-based sentiment analysis. IEEE Access, 9, 15561–15569. https://doi.org/10.1109/ACCESS.2021.3052937
Ahmad, F., & Guzmán, F. (2021). Negative online reviews, brand equity and emotional contagion. European Journal of Marketing, 55(11), 2825–2870. https://doi.org/10.1108/EJM-10-2019-0820
Al-Turaiki, I., & Altwaijry, N. (2021). A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection. Big Data, 9(3), 233–252. https://doi.org/10.1089/big.2020.0263
Ameur, A., Hamdi, S., & Ben Yahia, S. (2023). Sentiment Analysis for Hotel Reviews: A Systematic Literature Review. ACM Computing Surveys, 56(2). https://doi.org/10.1145/3605152
Aurangzeb, K., Ayub, N., & Alhussein, M. (2021). Aspect Based Multi-Labeling Using SVM Based Ensembler. IEEE Access, 9, 26026–26040. https://doi.org/10.1109/ACCESS.2021.3055768
Benlahbib, A., & Nfaoui, E. H. (2020). Aggregating customer review attributes for online reputation generation. IEEE Access, 8, 96550–96564. https://doi.org/10.1109/ACCESS.2020.2996805
Brzezi?ska-Wójcik, T. (2022). Service Quality Evaluation in Four-Star Hotels in Lublin Using the Contextual Analysis Method. Annales Universitatis Mariae Curie-Sklodowska. Sectio B, 77, 149–176. https://doi.org/10.17951/B.2022.77.0.149-176
Dogan, A., & Birant, D. (2021). Machine learning and data mining in manufacturing. In Expert Systems with Applications (Vol. 166). https://doi.org/10.1016/j.eswa.2020.114060
Ho, R. C., Withanage, M. S., & Khong, K. W. (2020). Sentiment drivers of hotel customers: a hybrid approach using unstructured data from online reviews. Asia-Pacific Journal of Business Administration, 12(3–4), 237–250. https://doi.org/10.1108/APJBA-09-2019-0192
Jeong, M., Shin, H. H., Lee, M., & Lee, J. (2023). Assessing brand performance consistency from consumer-generated media: the US hotel industry. International Journal of Contemporary Hospitality Management, 35(6), 2056–2083. https://doi.org/10.1108/IJCHM-12-2021-1516
Lai, X., Wang, F., & Wang, X. (2021). Asymmetric relationship between customer sentiment and online hotel ratings: the moderating effects of review characteristics. International Journal of Contemporary Hospitality Management, 33(6), 2137–2156. https://doi.org/10.1108/IJCHM-07-2020-0708
Molina-Coronado, B., Mori, U., Mendiburu, A., & Miguel-Alonso, J. (2020). Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process. IEEE Transactions on Network and Service Management, 17(4), 2451–2479. https://doi.org/10.1109/TNSM.2020.3016246
Nguyen, H. T. T., & Nguyen, T. X. (2023). Understanding customer experience with Vietnamese hotels by analyzing online reviews. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02098-8
Peng, Y. C., Cheng, M. S., & Hsu, P. Y. (2020). Constructing an Integrated Classifiers for Identifying Authenticity and Sentiment Analysis- A Case of Hotel Reviews. In Proceedings - 2020 International Computer Symposium, ICS 2020 (pp. 319–324). https://doi.org/10.1109/ICS51289.2020.00070
Pham, D. H. (2020). A latent aspect mining framework from textual reviews. Indian Journal of Computer Science and Engineering, 11(4), 347–359. https://doi.org/10.21817/indjcse/2020/v11i4/201104209
Ponnusamy, V., Humayun, M., Jhanjhi, N. Z., Yichiet, A., & Almufareh, M. F. (2021). Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks. Computer Systems Science and Engineering, 40(3), 1199–1215. https://doi.org/10.32604/CSSE.2022.018518
Rahmaningrum, S. A., & Oktaviana, P. P. (2020). Sentiment classification of hotel service review on traveloka sites using naïve bayes classifier (NBC) and binary logistic regression. In Journal of Physics: Conference Series (Vol. 1490, Issue 1). https://doi.org/10.1088/1742-6596/1490/1/012065
Rita, P., Ramos, R., Borges-Tiago, M. T., & Rodrigues, D. (2022). Impact of the rating system on sentiment and tone of voice: A Booking.com and TripAdvisor comparison study. International Journal of Hospitality Management, 104(2), 1–12. https://doi.org/10.1016/j.ijhm.2022.103245
Sánchez-Franco, M. J., & Rey-Tienda, S. (2024). The role of user-generated content in tourism decision-making: an exemplary study of Andalusia, Spain. Management Decision, 62(7), 2292–2328. https://doi.org/10.1108/MD-06-2023-0966
Sann, R., & Lai, P. C. (2020). Understanding homophily of service failure within the hotel guest cycle: Applying NLP-aspect-based sentiment analysis to the hospitality industry. International Journal of Hospitality Management, 91. https://doi.org/10.1016/j.ijhm.2020.102678
Sann, R., & Lai, P. C. (2021). Do expectations towards Thai hospitality differ? The views of English vs Chinese speaking travelers. International Journal of Culture, Tourism, and Hospitality Research, 15(1), 43–58. https://doi.org/10.1108/IJCTHR-01-2020-0010
Sann, R., Lai, P. C., Liaw, S. Y., & Chen, C. T. (2022). Predicting Online Complaining Behavior in the Hospitality Industry: Application of Big Data Analytics to Online Reviews. Sustainability (Switzerland), 14(3). https://doi.org/10.3390/su14031800
Sharma, H., & Aggarwal, A. G. (2021). The influence of user generated content on hotel sales: an Indian perspective. Journal of Modelling in Management, 16(4), 1358–1375. https://doi.org/10.1108/JM2-07-2020-0201
Shu, Z., Torralba, M. H., Carrasco, R. A., & López, M. F. B. (2023). Assessing customer satisfaction of London luxury hotels with the AHP method and the SERVPERF scale: a case study of customer reviews on TripAdvisor. In Procedia Computer Science (Vol. 221, pp. 73–80). https://doi.org/10.1016/j.procs.2023.07.011
Tanr?sevdi, A., Öztürk, G., & Öztürk, A. C. (2022). A supervised data mining approach for predicting comment card ratings. International Journal of Contemporary Hospitality Management, 34(5), 1823–1853. https://doi.org/10.1108/IJCHM-05-2021-0675
Thu, H. N. T. (2020). Measuring guest satisfaction from online reviews: Envidence in Vietnam. Cogent Social Sciences, 6(1). https://doi.org/10.1080/23311886.2020.1801117
Zhao, N., Gao, H., Wen, X., & Li, H. (2021). Combination of convolutional neural network and gated recurrent unit for aspect-based sentiment analysis. IEEE Access, 9, 15561–15569. https://doi.org/10.1109/ACCESS.2021.3052937
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