A Systematic Literature Review: Analyzing Service Quality Through User Reviews Using Machine Learning Approaches

Dwi Andre Vebriansyah; Budi Eko Soetjipto; Ludi Wisnuwardhana

doi:10.61132/rimba.v3i2.1770

Authors

Dwi Andre Vebriansyah Universitas Negeri Malang
Budi Eko Soetjipto Universitas Negeri Malang
Ludi Wisnuwardhana Universitas Negeri Malang

DOI:

https://doi.org/10.61132/rimba.v3i2.1770

Keywords:

Customer Reviews, Machine Learning, Sentiment Analysis, Service Quality

Abstract

This research conducted a systematic literature review of studies related to analyzing service quality based on user reviews with a machine learning approach. A total of 15 international and national journals were analyzed to identify challenges, methods, and trends in research in this aspect. The review results show that Natural Language Processing (NLP) and Sentiment Analysis techniques are the dominant approaches, with machine learning models such as Deep Learning, Naive Bayes, and Support Vector Machine (SVM) being commonly used. The review also identifies research gaps and provides recommendations for future research directions.

Downloads

Download data is not yet available.

References

Liu, Y., Bi, J. W., & Fan, Z. P. (2020). Deep learning for hotel service quality assessment based on user reviews. IEEE Transactions on Neural Networks and Learning Systems, 31(12), 5358-5372.

Sharma, A., Gandotra, E., & Gupta, D. (2022). A hybrid approach for e-commerce service quality assessment using NLP and machine learning. Expert Systems with Applications, 189, 116120.

Widodo, A., Pramudita, R., & Wibowo, A. (2021). Analisis kepuasan pelanggan e-commerce Indonesia menggunakan sentiment analysis dan topic modeling. Jurnal Informatika dan Sistem Informasi, 12(2), 78-92.

Park, S., & Kim, Y. (2019). Customer satisfaction prediction in the hospitality industry using deep learning. Tourism Management, 75, 287-297.

Jain, V. K., Kumar, S., & Fernandes, S. L. (2018). Extraction of emotions from multilingual text using intelligent text processing and computational linguistics. Journal of Computational Science, 25, 213-225.

Chen, L., & Wang, F. (2023). BERT-based multi-aspect service quality analysis from user reviews in e-commerce. International Journal of Machine Learning and Cybernetics, 14(1), 177-192.

Rodriguez, M., & Garcia, D. (2021). Service quality dimensions identification through aspect-based sentiment analysis in telecommunications. Journal of Service Research, 24(3), 380-397.

Hassan, M., Ali, T., & Ahmed, S. (2020). Machine learning approach for sentiment analysis in healthcare services. Health Informatics Journal, 26(2), 1176-1188.

Wijaya, A. P., & Santoso, H. B. (2022). Analisis sentimen ulasan pelanggan transportasi online menggunakan deep learning. Jurnal Ilmu Komputer dan Informasi, 15(1), 23-34.

Thompson, C. J., & Wilson, D. (2019). Automated service quality assessment through customer review analysis: A comparative study of machine learning algorithms. Journal of Service Management, 30(3), 341-360.

Zhang, Y., & Lee, H. (2021). Transfer learning for cross-domain service quality analysis. Information Processing & Management, 58(3), 102496.

Brown, A., & Taylor, M. (2022). Explainable AI methods for hotel service quality improvement. International Journal of Hospitality Management, 104, 103237.

Kumar, R., & Srivastava, S. (2022). Multi-aspect sentiment analysis for E-commerce service quality using attention-based deep learning. Neural Computing and Applications, 34(9), 6789-6805.

Nugroho, D. A., & Winarno, E. (2023). Implementasi sentiment analysis pada ulasan aplikasi transportasi online menggunakan algoritma support vector machine. Jurnal Teknologi Informasi dan Ilmu Komputer, 10(2), 487-496.

Lee, S., & Park, J. (2020). Enhancing service quality perception through big data analytics: A study of airline online reviews. Journal of Business Research, 113, 422-435.