Application of Machine Learning Methods in Customer Satisfaction Measurement based on Online Comments
DOI:
https://doi.org/10.24311/jabes/2023.34.5.1Keywords:
Machine learning, Satisfaction measurement, Opinion miningAbstract
This study proposes the application of a machine learning method to measure customer satisfaction based on online comments. Research data is the reviews of travelers on Agoda.com about hotels in Vietnam. The study conducts training using machine learning models: Neural Network (NN), Decision Tree (DT), Naïve Bayes (NB), Support Vector Machines (SVM), Logistic Regression (LR), and Random Forest (RF) to find the best classification model, then apply this model to predict the customer satisfaction classification across the entire dataset. Next, the study uses the method of aggregating and ranking customer satisfaction. This study has reference value for measuring customer satisfaction in business fields.
References
Almuqren, L., & Cristea, A. I. (2022). Predicting STC customers’ satisfaction using Twitter. IEEE Transactions on Computational Social Systems, 10(1), 204–210.
Bashir, S., Afzal, W., & Baig, A. R. (2016). Opinion-based entity ranking using learning to rank. Applied Soft Computing, 38, 151–163.
Becser, N., & Zoltay-Paprika, Z. (2003). A Decision Support Model for Improving Service Quality, SQI-DSS: A New Approach (Working Paper). Budapest University of Economics and Public Administration, Department of Business Economics.
Binali, H., Potdar, V., & Wu, C. (2009). A state of the art opinion mining and its application domains. In Industrial Technology, 2009. ICIT 2009. IEEE International Conference on. IEEE.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.
Dhokrat, A., Khillare, S., & Mahender, C. N. (2015). Review on techniques and tools used for opinion mining. International Journal of Computer Applications Technology and Research, 4(6),
419–424.
Farhadloo, M., Patterson, R. A., & Rolland, E. (2016). Modeling customer satisfaction from unstructured data using a Bayesian approach. Decision Support Systems, 90, 1–11.
Hussain, Y. (2019). Predicting customer satisfaction with product reviews: A comparitive study of some machine learning approaches (Master's thesis). Faculty of Information Technology and Communication Sciences, Tampere University.
Jung, Y., & Suh, Y. (2019). Mining the voice of employees: A text mining approach to identifying and analyzing job satisfaction factors from online employee reviews. Decision Support Systems, 123, 113074.
Lee, J., Park, D.-H., & Han, I. (2011). The different effects of online consumer reviews on consumers’ purchase intentions depending on trust in online shopping malls: An advertising perspective. Internet Research, 21(2), 187–206.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113.
Mudambi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon.com. MIS Quarterly, 34(1), 185–200.
Oh, S., Ji, H., Kim, J., Park, E., & del Pobil, A. P. (2022). Deep learning model based on expectation-confirmation theory to predict customer satisfaction in hospitality service. Information Technology & Tourism, 24(1), 109–126.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.
Phung, T. K., Te, N. A., & Ha, T. T. T. (2021). A machine learning approach for opinion mining online customer reviews. In 2021 21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter), (pp. 243–246).
Piccoli, G., & Pigni, F. (2013). Harvesting external data: The potential of digital data streams. MIS Quarterly Executive, 12(1), 143–154.
Sezgen, E., Mason, K. J., & Mayer, R. (2019). Voice of airline passenger: A text mining approach to understand customer satisfaction. Journal of Air Transport Management, 77, 65–74.
Shawe-Taylor, J., & Sun, S. (2011). A review of optimization methodologies in support vector machines. Neurocomputing, 74(17), 3609–3618.
Stylios, G., Christodoulakis, D., Besharat, J., Vonitsanou, M., Kotrotsos, I., Koumpouri, A., & Stamou, S. (2010). Public opinion mining for governmental decisions. Electronic Journal of e-Government, 8(2), 203–214.
Sun, S., Luo, C., & Chen, J. (2017). A review of natural language processing techniques for opinion mining systems. Information Fusion, 36, 10–25.
Thu, H. N. T. (2020). Measuring guest satisfaction from online reviews: Evidence in Vietnam. Cogent Social Sciences, 6(1), 1801117.
Ting, P. J. L., Chen, S. L., Chen, H., & Fang, W. C. (2017). Using big data and text analytics to understand how customer experiences posted on yelp.com impact the hospitality industry. Contemporary Management Research, 13(2). doi: 10.7903/cmr.17730
Wang, Y. (2016). More important than ever: Measuring tourist satisfaction. Griffith Institute for Tourism Research Report Series Report No. 10. Griffith University
Wanner, F., Rohrdantz, C., Mansmann, F., Oelke, D., & Keim, D. A. (2009). Visual sentiment analysis of RSS news feeds featuring the us presidential election in 2008. Retrieved from https://kops.uni-konstanz.de/server/api/core/bitstreams/29e30b9f-8714-45d5-9f7e-46eb9811381f/content
Yadav, S. K. (2015). Sentiment analysis and classification: A survey. International Journal of Advance Research in Computer Science and Management Studies, 3(3), 113–121.
Yussupova, N., Kovács, G., Boyko, M., & Bogdanova, D. (2016). Models and methods for quality management based on artificial intelligence applications. Acta Polytechnica Hungarica, 13(3), 45–60.
Zhao, Y., Xu, X., & Wang, M. (2019). Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews. International Journal of Hospitality Management, 76, 111–121.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 JOURNAL OF ASIAN BUSINESS AND ECONOMIC STUDIES
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
