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Kazemi Esfe, Pedram, Noorbehbahani, Fakhroddin. (1405). Behavioral Loyalty Prediction from Customer Reviews in E-Commerce: A Deep Learning and Explainable AI Approach. , 13(1), 35-57. doi: 10.22108/jcs.2026.147672.1188
Pedram Kazemi Esfe; Fakhroddin Noorbehbahani. "Behavioral Loyalty Prediction from Customer Reviews in E-Commerce: A Deep Learning and Explainable AI Approach". , 13, 1, 1405, 35-57. doi: 10.22108/jcs.2026.147672.1188
Kazemi Esfe, Pedram, Noorbehbahani, Fakhroddin. (1405). 'Behavioral Loyalty Prediction from Customer Reviews in E-Commerce: A Deep Learning and Explainable AI Approach', , 13(1), pp. 35-57. doi: 10.22108/jcs.2026.147672.1188
Kazemi Esfe, Pedram, Noorbehbahani, Fakhroddin. Behavioral Loyalty Prediction from Customer Reviews in E-Commerce: A Deep Learning and Explainable AI Approach. , 1405; 13(1): 35-57. doi: 10.22108/jcs.2026.147672.1188

Behavioral Loyalty Prediction from Customer Reviews in E-Commerce: A Deep Learning and Explainable AI Approach

Journal of Computing and Security
مقاله 4، دوره 13، شماره 1، فروردین 2026، صفحه 35-57    اصل مقاله (4.19 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22108/jcs.2026.147672.1188
نویسندگان
Pedram Kazemi Esfe؛ Fakhroddin Noorbehbahani*
Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.
چکیده
Understanding and predicting customer loyalty has become essential in increasingly competitive digital markets, yet conventional behavioral models, such as RFM, remain limited by their reliance on retrospective transactional data. Meanwhile, vast volumes of rich, interactive customer feedback, particularly online reviews, remain chronically underutilized for loyalty prediction. This study introduces a novel deep learning framework that predicts behavioral loyalty directly from customer review texts, eliminating the need for historical purchase records. Using a large e-commerce dataset of 58,841 samples, we combine advanced transformer-based language models (BERT, RoBERTa, ALBERT, GPT, and Qwen) with structured features and employ a genetic algorithm to optimize across 19,440 configurations of loyalty labeling schemes, neural architectures, text embeddings, and feature combinations. The resulting model achieves an accuracy of 0.92 and an F1-score of 0.90, substantially outperforming baseline machine learning methods and non-transformer text embedding approaches. Beyond performance, the integration of SHAP-based explainability provides transparent insights into the linguistic and behavioral cues driving loyalty predictions. These findings demonstrate that customer reviews contain powerful behavioral signals that can be transformed into actionable intelligence for real-time CRM, customer retention, competitive analysis, and multi-channel personalization, particularly in contexts where RFM data are incomplete or unavailable. This work offers both a methodological advancement in loyalty prediction and a practical, scalable tool for organizations seeking to leverage interactive feedback to build stronger, more proactive customer relationships.
کلیدواژه‌ها
Loyalty Prediction؛ Customer Review Analysis؛ Natural Language Processing؛ RFM Model؛ Explainable AI
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مراجع
[1] Adnan Aktepe and Süleyman Ersöz and Bilal Toklu. Customer satisfaction and loyalty analysis with classification algorithms and Structural Equation Modeling. Computers \& Industrial Engineering. Applications of Computational Intelligence and Fuzzy Logic to Manufacturing and Service Systems. 86: 95-106, 2015. [DOI ]
[2] Wei, Jo-Ting and Lin, Shih-Yen and Wu, Hsin-Hung. A review of the application of RFM model. African journal of business management. 4(19): 4199, Academic Journals. 2010.
[3] Kilimci, Zeynep Hilal. Prediction of user loyalty in mobile applications using deep contextualized word representations. Journal of Information and Telecommunication. 6(1): 43--62, Taylor \& Francis. 2022. [DOI ]
[4] Wassouf, Wissam Nazeer and Alkhatib, Ramez and Salloum, Kamal and Balloul, Shadi. Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study. Journal of Big Data. 7(1): 29, Springer. 2020. [DOI ]
[5] Vemberain, Jesselin and Rakhman, Abdullah. Influence of promotions, price perceptions, service quality towards customer loyalty through customer satisfaction Gojek in Jakarta. Jurnal Ekonomi Perusahaan. 31(1): 21--40, 2024. [DOI ]
[6] Hsu, TH and Lin, LZ and Chiu, SY. Customer loyalty program based on quality function deployment with fuzzy linguistic preference relation. Iranian Journal of Fuzzy Systems. 17(1): 105--120, University of Sistan and Baluchestan. 2020.
[7] Cheng, Shih-I. Comparisons of competing models between attitudinal loyalty and behavioral loyalty. International Journal of Business and Social Science. 2(10): 149--166, 2011.
[8] Patel, Nikhil and Trivedi, Sandeep. Leveraging predictive modeling, machine learning personalization, NLP customer support, and AI chatbots to increase customer loyalty. Empirical Quests for Management Essences. 3(3): 1--24, 2020.
[9] Wang, Chih-Hsuan. Apply robust segmentation to the service industry using kernel induced fuzzy clustering techniques. Expert systems with applications. 37(12): 8395--8400, Elsevier. 2010. [DOI ]
[10] Zaw, Myint and Tandayya, Pichaya. Product Categorization for Social Marketing Applying the RFC Model and Data Science Techniques. International Journal of Business Analytics (IJBAN). 7(4): 43--62, IGI Global Scientific Publishing. 2020. [DOI ]
[11] Kishada, Zeyad M and Wahab, Norailis AB and Mustapha, Aouache. CUSTOMER LOYALTY ASSESSMENT IN MALAYSIAN ISLAMIC BANKING USING ARTIFICIAL INTELLIGENCE.. Journal of Theoretical \& Applied Information Technology. 87(1): 2016.
[12] Alizadeh, Hossein and Bidgoli, B Minaei. Introducing a hybrid data mining model to evaluate customer loyalty. Engineering, Technology \& Applied Science Research. 6(6): 1235--1240, 2016. [DOI ]
[13] Machado, Marcos Roberto and Karray, Salma and De Sousa, Ivaldo Tributino. LightGBM: An effective decision tree gradient boosting method to predict customer loyalty in the finance industry. 2019 14th international conference on computer science \& education (ICCSE). 1111--1116, 2019. [DOI ]
[14] Rahul, T and Majhi, R. An adaptive nonlinear approach for estimation of consumer satisfaction and loyalty in mobile phone sector of India. Journal of Retailing and Consumer Services. 21(4): 570--580, Elsevier. 2014. [DOI ]
[15] Sulistiani, Heni and Muludi, Kurnia and Syarif, Admi. Implementation of dynamic mutual information and support vector machine for customer loyalty classification. Journal of Physics: Conference Series. 1338(1): 012050, 2019. [DOI ]
[16] Figueroa, Cristi{\'a}n J. Predicting customer loyalty labels in a large retail database: A case study in Chile. Data Mining: Special Issue in Annals of Information Systems. 229--253, Springer. 2009. [DOI ]
[17] Granov, Anida. Customer loyalty, return and churn prediction through machine learning methods: for a Swedish fashion and e-commerce company. 2021.
[18] Lee, Hiu Fai and Jiang, Ming. A hybrid machine learning approach for customer loyalty prediction. International Conference on Neural Computing for Advanced Applications. 211--226, 2021. [DOI ]
[19] Abbassy, Mohamed M. Using machine learning technique for analytical customer loyalty. International Journal of Computer Science \& Network Security. 23(8): 190--198, International Journal of Computer Science \& Network Security. 2023.
[20] Qin, Xujie. Research on loyalty prediction of e-commerce customer based on data mining. Applied Mathematics and Nonlinear Sciences. 8(2): 721--732, Sciendo. 2023. [DOI ]
[21] Heni Sulistiani and Aris Tjahyanto. Comparative Analysis of Feature Selection Method to Predict Customer Loyalty. IPTEK The Journal of Engineering. 3(1): 2017. [DOI ]
[22] Wijaya, Andri and Girsang, Abba Suganda. Use of data mining for prediction of customer loyalty. Communication and Information Technology Journal. 10(1): 41--47, Bina Nusantara University. 2016. [DOI ]
[23] Syukur, Abdul and Wahono, Romi Satria and Naufal, Abdul Razak and Supriyanto, Catur. Bootstrapping and Weighted Information Gain in Support Vector Machine for Customer Loyalty Prediction. Journal of Internet Banking and Commerce. 23(1): 1--13, ARRAY Development. 2018.
[24] Tong, Lili and Wang, Yiting and Wen, Fan and Li, Xiaowen. The research of customer loyalty improvement in telecom industry based on NPS data mining. China Communications. 14(11): 260--268, IEEE. 2017. [DOI ]
[25] Hamdan, Iskandar Zul Putera and Othman, Muhaini. Predicting customer loyalty using machine learning for hotel industry. Journal of Soft Computing and Data Mining. 3(2): 31--42, 2022. [DOI ]
[26] Hsu, Chi-I and Shih, Meng-Long and Huang, Biing-Wen and Lin, Bing-Yi and Lin, Chun-Nan. Predicting tourism loyalty using an integrated Bayesian network mechanism. Expert Systems with Applications. 36(9): 11760--11763, Elsevier. 2009. [DOI ]
[27] Hamdan, Iskandar Zul Putera and Othman, Muhaini and Hassim, Yana Mazwin Mohmad and Marjudi, Suziyanti and Yusof, Munirah Mohd. Customer loyalty prediction for hotel industry using machine learning approach. JOIV: International Journal on Informatics Visualization. 7(3): 695--703, 2023. [DOI ]
[28] Singh, Balgopal. Predicting airline passengers’ loyalty using artificial neural network theory. Journal of air transport management. 94: 102080, Elsevier. 2021. [DOI ]
[29] Wu, Jain-Shing and Chien, Ting-Hsuan and Chien, Li-Ren and Yang, Chin-Yi. Using artificial intelligence to predict class loyalty and plagiarism in students in an online blended programming course during the COVID-19 pandemic. Electronics. 10(18): 2203, MDPI. 2021. [DOI ]
[30] Wang, Chuang and Zhou, Rongxin and Lee, Matthew KO. Can loyalty be pursued and achieved? An extended RFD model to understand and predict user loyalty to mobile apps. Journal of the Association for Information Science and Technology. 72(7): 824--838, Wiley Online Library. 2021. [DOI ]
[31] Noorbehbahani, Fakhroddin and Bajoghli, Soroush and Esfahani, Hooman Hoghooghi. Customer Loyalty Prediction of E-marketplaces Via Review Analysis. 2023 9th International Conference on Web Research (ICWR). 311--316, 2023. [DOI ]
[32] Ghani, Usman and Bajwa, Imran Sarwar and Ashfaq, Aimen. A fuzzy logic based intelligent system for measuring customer loyalty and decision making. Symmetry. 10(12): 761, MDPI. 2018. [DOI ]
[33] Shahid, Rumana and Mozumder, Md Abu Sufian and Sweet, Md Murshid Reja and Hasan, Mehedi and Alam, Mahfuz and Rahman, Mohammad Anisur and Prabha, Mani and Arif, Md and Ahmed, Md Parvez and Islam, Md Rafiqul. Predicting customer loyalty in the airline industry: a machine learning approach integrating sentiment analysis and user experience. International Journal on Computational Engineering. 1(2): 50--54, 2024. [DOI ]
[34] Khan, Rida and Urolagin, Siddhaling. Airline sentiment visualization, consumer loyalty measurement and prediction using twitter data. International journal of advanced computer science and applications. 9(6): Science and Information (SAI) Organization Limited. 2018. [DOI ]
[35] Urolagin, Siddhaling and Patel, Saifali. User-specific loyalty measure and prediction using deep neural network from Twitter Data. IEEE Transactions on Computational Social Systems. 11(1): 1046--1061, IEEE. 2023. [DOI ]
[36] Zaki, Mohamed and Kandeil, Dalia and Neely, Andy and McColl-Kennedy, Janet R. The fallacy of the net promoter score: Customer loyalty predictive model. Cambridge Service Alliance. 10(1): 1--25, 2016.
[37] Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos. " Why should i trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 1135--1144, 2016. [DOI ]
[38] Lundberg, Scott M and Lee, Su-In. A unified approach to interpreting model predictions. Advances in neural information processing systems. 30: 2017.
[39] Avelino, Mariana and Macketanz, Vivien and Avramidis, Eleftherios and M{\"o}ller, Sebastian. A test suite for the evaluation of Portuguese-English machine translation. International Conference on Computational Processing of the Portuguese Language. 15--25, 2022. [DOI ]
[40] Miller, George A. WordNet: a lexical database for English. Communications of the ACM. 38(11): 39--41, ACM New York, NY, USA. 1995. [DOI ]
[41] Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina. Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). 4171--4186, 2019.
[42] Zhenzhong Lan and Mingda Chen and Sebastian Goodman and Kevin Gimpel and Piyush Sharma and Radu Soricut. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. 2020. [DOI ]
[43] Radford, Alec and Wu, Jeffrey and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya. Language Models are Unsupervised Multitask Learners. OpenAI. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf. Accessed: 2024-11-15. 2019.
[44] Bai, Jinze and Bai, Shuai and Chu, Yunfei and Cui, Zeyu and Dang, Kai and Deng, Xiaodong and Fan, Yang and Ge, Wenbin and Han, Yu and Huang, Fei and others. Qwen technical report. arXiv preprint arXiv:2309.16609. 2023.
[45] Huang, Xin and Khetan, Ashish and Cvitkovic, Milan and Karnin, Zohar. Tabtransformer: Tabular data modeling using contextual embeddings. arXiv preprint arXiv:2012.06678. 2020. [DOI ]
[46] Mosca, Edoardo and Szigeti, Ferenc and Tragianni, Stella and Gallagher, Daniel and Groh, Georg. SHAP-based explanation methods: a review for NLP interpretability. Proceedings of the 29th international conference on computational linguistics. 4593--4603, 2022.
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