How to cite this paper
Nurfaizah, R., Ahsan, M & Le, M. (2025). A hybrid approach to hospital quality monitoring based on google maps reviews: Integrating p-control charts and bidirectional encoder representations from transformers (BERT).International Journal of Data and Network Science, 9(4), 1081-1106.
Refrences
Alammar, J. (2018). The Illustrated Transformer. Github. https://jalammar.github.io/illustrated-transformer/
Alaparthi, S., & Mishra, M. (2021). BERT: A sentiment analysis odyssey. Journal of Marketing Analytics, 9(2), 118–126.
AlBadani, B., Shi, R., & Dong, J. (2022). A novel machine learning approach for sentiment analysis on Twitter incorporating the universal language model fine-tuning and SVM. Applied System Innovation, 5(1), 13.
Allibhai, J. (2018). Hold-out vs. Cross-validation in Machine Learning. Medium. https://medium.com/@jaz1/holdout-vs-cross-validation-in-machine-learning-7637112d3f8f
Bekkar, M., Djemaa, H. K., & Alitouche, T. A. (2013). Evaluation Measures for Models Assessment over Imbalanced Data Sets. Journal of Information Engineering and Applications, 3(10), 27–38.
Bello, A., Ng, S.-C., & Leung, M.-F. (2023). A BERT framework to sentiment analysis of tweets. Sensors, 23(1), 506.
Borg, A., & Boldt, M. (2020). Using VADER sentiment and SVM for predicting customer response sentiment. Expert Systems with Applications, 162, 113746. https://doi.org/https://doi.org/10.1016/j.eswa.2020.113746
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for lan-guage understanding. NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computa-tional Linguistics: Human Language Technologies - Proceedings of the Conference, 1(Mlm), 4171–4186.
Elfaik, H., & Nfaoui, E. H. (2020). Deep bidirectional LSTM network learning-based sentiment analysis for Arabic text. Jour-nal of Intelligent Systems, 30(1), 395–412.
Feldman, R., & Sanger, J. (2006). The Text Mining Handbook:Advanced Approaches in Analyzing Unstructured Data. In The Text Mining Handbook. Cambridge University Press. https://doi.org/10.1017/cbo9780511546914
Firmansyah, D., & Ahsan, M. (2023). Monitoring Kualitas Pada Aplikasi MyPertamina Berdasarkan Rating Pengguna di Google Play Menggunakan Diagram Kendali p. Jurnal Sains Dan Seni ITS, 12(2), D158–D163.
Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. In Data Mining: Concepts and Techniques (3rd ed.). Elsevier Inc. https://doi.org/10.1016/C2009-0-61819-5
Huang, X., Zhang, W., Tang, X., Zhang, M., Surbiryala, J., Iosifidis, V., Liu, Z., & Zhang, J. (2021). Lstm based sentiment analysis for cryptocurrency prediction. Database Systems for Advanced Applications: 26th International Conference, DASFAA 2021, Taipei, Taiwan, April 11–14, 2021, Proceedings, Part III 26, 617–621.
Jin, Z., Yang, Y., & Liu, Y. (2020). Stock closing price prediction based on sentiment analysis and LSTM. Neural Computing and Applications, 32, 9713–9729.
Khan, A., & Baharudin, B. (2011). Sentiment classification using sentence-level semantic orientation of opinion terms from blogs. 2011 National Postgraduate Conference - Energy and Sustainability: Exploring the Innovative Minds, NPC 2011, 1–7. https://doi.org/10.1109/NatPC.2011.6136319
Kokab, S. T., Asghar, S., & Naz, S. (2022). Transformer-based deep learning models for the sentiment analysis of social media data. Array, 14, 100157.
Li, Z., Li, R., & Jin, G. (2020). Sentiment analysis of danmaku videos based on naïve bayes and sentiment dictionary. Ieee Ac-cess, 8, 75073–75084.
Michel, S. (2001). Analyzing service failures and recoveries: A process approach. International Journal of Service Industry Management, 12(1), 20–33. https://doi.org/10.1108/09564230110382754
Montgomery, D. (2013). Introduction to Statistical Quality Control Seventh Edition (Issue 112). John Wiley & Sons Inc.
Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining, 11(1), 81. https://doi.org/10.1007/s13278-021-00776-6
Obiedat, R., Qaddoura, R., Al-Zoubi, A. M., Al-Qaisi, L., Harfoushi, O., Alrefai, M., & Faris, H. (2022). Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution. IEEE Ac-cess, 10, 22260–22273. https://doi.org/10.1109/ACCESS.2022.3149482
Pota, M., Ventura, M., Catelli, R., & Esposito, M. (2020). An effective BERT-based pipeline for Twitter sentiment analysis: A case study in Italian. Sensors, 21(1), 133.
Pribadi, N. A., & Ahsan, M. (2023). Monitoring Quality of KAI Access Application Based on Customer Reviews on Google Play Store Using Laney p’ Control Chart Based on Convolutional Neural Network. Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023), 175–184. https://doi.org/10.2991/978-94-6463-332-0_20
Pristiyono, Ritonga, M., Ihsan, M. A. Al, Anjar, A., & Rambe, F. H. (2021). Sentiment analysis of COVID-19 vaccine in Indo-nesia using Naïve Bayes Algorithm. IOP Conference Series: Materials Science and Engineering, 1088(1), 012045.
Pyon, C. U., Woo, J. Y., & Park, S. C. (2011). Service improvement by business process management using customer com-plaints in financial service industry. Expert Systems with Applications, 38(4), 3267–3279. https://doi.org/10.1016/j.eswa.2010.08.112
Rasouli, O., & Zarei, M. H. (2016). Monitoring and reducing patient dissatisfaction: a case study of an Iranian public hospital. Total Quality Management and Business Excellence, 27(5–6), 531–559. https://doi.org/10.1080/14783363.2015.1016869
Singh, M., Jakhar, A. K., & Pandey, S. (2021). Sentiment analysis on the impact of coronavirus in social life using the BERT model. Social Network Analysis and Mining, 11(1), 33.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 2017-December(Nips), 5999–6009.
Villavicencio, C., Macrohon, J. J., Inbaraj, X. A., Jeng, J.-H., & Hsieh, J.-G. (2021). Twitter sentiment analysis towards covid-19 vaccines in the Philippines using naïve bayes. Information, 12(5), 204.
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55(7), 5731–5780. https://doi.org/10.1007/s10462-022-10144-1
Wilie, B., Vincentio, K., Winata, G. I., Cahyawijaya, S., Li, X., Lim, Z. Y., Soleman, S., Mahendra, R., Fung, P., Bahar, S., & Purwarianti, A. (2020). IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding.
Xu, X., & Li, Y. (2016). The antecedents of customer satisfaction and dissatisfaction toward various types of hotels: A text mining approach. International Journal of Hospitality Management, 55, 57–69. https://doi.org/10.1016/j.ijhm.2016.03.003