Hybrid ABSA–C5.0 framework for interpretable classification of tourist perceptions in digital destination services
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Abstract
This study proposes an interpretable ABSA–C5.0 hybrid framework for analyzing tourists’ perceptions of digital destination information services. This framework integrates Aspect-Based Sentiment Analysis (ABSA) for aspect extraction and sentiment assessment with C5.0 decision trees for classification and rule generation. This study follows the Knowledge Discovery in Databases (KDD) process, including preprocessing, feature engineering, modeling, and evaluation. The experiment uses a statistically synthesized dataset containing 72 labeled reviews, designed to reflect real-world online review patterns. With a 70:30 validation split, the model achieved 97.22% accuracy and a Cohen’s Kappa value of 0.947 in this controlled setting. However, these results are not intended for generalization, given the limited dataset size, synthetic data construction, and the absence of baseline comparisons and statistical significance tests. The extracted rules indicate that interactivity, clarity, and response speed are the primary factors driving positive perceptions. This framework is suitable for exploratory analysis, while further validation with real-world data and comparative models is required.
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How to Cite
Riandari, F., Ginting, R., Sulistianingsih, I., Tasril, V., & Rizka, A. (2026). Hybrid ABSA–C5.0 framework for interpretable classification of tourist perceptions in digital destination services. International Journal of Basic and Applied Science, 15(1), 21–32. https://doi.org/10.35335/ijobas.v15i1.856
References
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[25] P. Liberato, E. Alen, and D. Liberato, “Smart tourism destination triggers consumer experience: the case of Porto,” Eur. J. Manag. Bus. Econ., vol. 27, no. 1, pp. 6–25, 2018, doi: 10.1108/EJMBE-11-2017-0051.
[26] C. Wang, H. Qu, and M. K. Hsu, “Toward an integrated model of tourist expectation formation and gender difference,” Tour. Manag., vol. 54, no. 6, pp. 58–71, 2016, doi: 10.1016/j.tourman.2015.10.009.
[27] S. Khademi, F. Rafieinasab, and N. Radhakrishnan, “Data mining in academic libraries: a systematic review,” Int. J. Inf. Sci. Manag., vol. 21, no. 3, pp. 255–271, 2023.
[28] K. Kowsari, K. J. Meimandi, M. Heidarysafa, and S. Mendu, “Text Classification Algorithms : A Survey,” Information, vol. 10, no. 4, pp. 1–68, 2019, doi: 10.3390/info10040150.
[29] T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing,” ieee Comput. Intell. Mag., vol. 13, no. 3, pp. 55–75, 2018, doi: 10.1109/MCI.2018.2840738.
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[31] M. T. Ribeiro, “‘ Why Should I Trust You ?’ Explaining the Predictions of Any Classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2026, no. March, pp. 1135–1144. doi: 10.1145/2939672.2939778.
[32] R. Guidotti, “A Survey of Methods for Explaining Black Box Models A Survey of Methods for Explaining Black Box Models,” ACM Comput. Surv., vol. 51, no. 5, pp. 1–93, 2026, doi: 10.1145/3236009.
[33] Y. Xia et al., “Sustainable service quality assessment of Chinese healthcare e-government: a multi-criteria decision framework based on SERVQUAL model and entropy-weight TOPSIS method,” Front. Digit. Heal., vol. 7, p. 1611979, 2025, doi: 10.3389/fdgth.2025.1611979.
[2] Z. Yi Wang, H. Wenyuan, P. Kumari, F. Tian, and S. Zhang, “The Influence of Smart Tourism Technology on Use Intention, Perceived Value, and Tourists’ Net Benefits,” J. Qual. Assur. Hosp. Tour., pp. 1–16, 2025, doi: 10.1080/1528008X.2025.2488428.
[3] C.-M. Chuang, “The conceptualization of smart tourism service platforms on tourist value co-creation behaviours: an integrative perspective of smart tourism services,” Humanit. Soc. Sci. Commun., vol. 10, no. 1, pp. 1–16, 2023, doi: 10.1057/s41599-023-01867-9.
[4] F. Riandari, S. Defit, and Yuhandri, “Artificial Intelligence Approach for Smart Sharia Tourism: a Review,” J. Theor. Appl. Inf. Technol., vol. 100, no. 13, pp. 4932–4940, 2022.
[5] D. Erdoğan et al., “Developing a Deep Learning-Based Sentiment Analysis System of Hotel Customer Reviews for Sustainable Tourism,” Sustainability, vol. 17, no. 13, p. 5756, 2025, doi: 10.3390/su17135756.
[6] Z.-A. Torabi, M. Pourtaheri, C. M. Hall, A. Sharifi, and F. Javidi, “Smart tourism technologies, revisit intention, and word-of-mouth in emerging and smart rural destinations,” Sustainability, vol. 15, no. 14, p. 10911, 2023, doi: 10.3390/su151410911.
[7] C. Pai, S. Kang, Y. Liu, and Y. Zheng, “An examination of revisit intention based on perceived smart tourism technology experience,” Sustainability, vol. 13, no. 2, p. 1007, 2021, doi: 10.3390/su13021007.
[8] C.-K. Pai, Y. Liu, S. Kang, and A. Dai, “The role of perceived smart tourism technology experience for tourist satisfaction, happiness and revisit intention,” Sustainability, vol. 12, no. 16, p. 6592, 2020, doi: 10.3390/su12166592.
[9] I. Perikos and A. Diamantopoulos, “Explainable aspect-based sentiment analysis using transformer models,” Big Data Cogn. Comput., vol. 8, no. 11, p. 141, 2024, doi: 10.3390/bdcc8110141.
[10] S. O. E. Putri, A. A. Arifiyanti, and A. R. E. Najaf, “Convolutional Neural Network Approach for Aspect-Based Sentiment Analysis of Tourism Reviews,” bit-Tech, vol. 8, no. 1, pp. 448–458, 2025, doi: 10.32877/bt.v8i1.2582.
[11] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in 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), 2019, pp. 4171–4186.
[12] C. X. Chu, M. Planck, C. X. Chu, S. Razniewski, and G. Weikum, “TiFi : Taxonomy Induction for Fictional Domains TiFi : Taxonomy Induction for Fictional Domains ∗,” in The World Wide Web Conference, 2026, no. March, pp. 2673–2679. doi: 10.1145/3308558.3313519.
[13] K. Puh and M. Bagić Babac, “Predicting sentiment and rating of tourist reviews using machine learning,” J. Hosp. Tour. insights, vol. 6, no. 3, pp. 1188–1204, 2023, doi: 10.1108/JHTI-02-2022-0078.
[14] M. Berrada, “Peeking Inside the Black-Box : A Survey on Explainable Artificial Intelligence ( XAI ),” in IEEE Access, 2018, vol. 6, pp. 52138–52160. doi: 10.1109/ACCESS.2018.2870052.
[15] A. M. Elsayad, A. M. Nassef, and M. Al-dhaifallah, “Classification of Biodegradable Substances Using Balanced Random Trees and Boosted C5 . 0 Decision Trees,” Environ. Sci. Eng., vol. 17, no. 24, pp. 1–20, 2020, doi: 10.3390/ijerph17249322.
[16] M. Batra and R. Agrawal, “Comparative analysis of decision tree algorithms,” in Nature Inspired Computing: Proceedings of CSI 2015, Springer, 2017, pp. 31–36. doi: 10.1007/978-981-10-6747-1_4.
[17] O. Maimon and L. Rokach, Soft computing for knowledge discovery and data mining. Springer Science & Business Media, 2007. doi: 10.1007/978-0-387-69935-6.
[18] E. Pashaei, M. Ozen, and N. Aydin, “Improving medical diagnosis reliability using Boosted C5. 0 decision tree empowered by Particle Swarm Optimization,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, pp. 7230–7233. doi: 10.1109/EMBC.2015.7320060.
[19] Z. Mohammadi-Pirouz, K. Hajian-Tilaki, M. Sadeghi Haddat-Zavareh, A. Amoozadeh, and S. Bahrami, “Development of decision tree classification algorithms in predicting mortality of COVID-19 patients,” Int. J. Emerg. Med., vol. 17, no. 1, p. 126, 2024, doi: 10.1186/s12245-024-00681-7.
[20] Z. Azam, M. M. Islam, and M. N. Huda, “Comparative analysis of intrusion detection systems and machine learning-based model analysis through decision tree,” Ieee Access, vol. 11, pp. 80348–80391, 2023, doi: 10.1109/ACCESS.2023.3296444.
[21] B. Charbuty and A. Abdulazeez, “Classification based on decision tree algorithm for machine learning,” J. Appl. Sci. Technol. trends, vol. 2, no. 01, pp. 20–28, 2021, doi: 10.38094/jastt20165.
[22] H. Oh and S. Lee, “Evaluation and interpretation of tourist satisfaction for local Korean festivals using explainable AI,” Sustainability, vol. 13, no. 19, p. 10901, 2021, doi: 10.3390/su131910901.
[23] M. J. Sánchez-Franco and S. Rey-Tienda, “The role of user-generated content in tourism decision-making: an exemplary study of Andalusia, Spain,” Manag. Decis., vol. 62, no. 7, pp. 2292–2328, 2024, doi: 10.1108/MD-06-2023-0966.
[24] A. García-Milon, E. Juaneda-Ayensa, C. Olarte-Pascual, and J. Pelegrín-Borondo, “Towards the smart tourism destination: Key factors in information source use on the tourist shopping journey,” Tour. Manag. Perspect., vol. 36, p. 100730, 2020, doi: 10.1016/j.tmp.2020.100730.
[25] P. Liberato, E. Alen, and D. Liberato, “Smart tourism destination triggers consumer experience: the case of Porto,” Eur. J. Manag. Bus. Econ., vol. 27, no. 1, pp. 6–25, 2018, doi: 10.1108/EJMBE-11-2017-0051.
[26] C. Wang, H. Qu, and M. K. Hsu, “Toward an integrated model of tourist expectation formation and gender difference,” Tour. Manag., vol. 54, no. 6, pp. 58–71, 2016, doi: 10.1016/j.tourman.2015.10.009.
[27] S. Khademi, F. Rafieinasab, and N. Radhakrishnan, “Data mining in academic libraries: a systematic review,” Int. J. Inf. Sci. Manag., vol. 21, no. 3, pp. 255–271, 2023.
[28] K. Kowsari, K. J. Meimandi, M. Heidarysafa, and S. Mendu, “Text Classification Algorithms : A Survey,” Information, vol. 10, no. 4, pp. 1–68, 2019, doi: 10.3390/info10040150.
[29] T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing,” ieee Comput. Intell. Mag., vol. 13, no. 3, pp. 55–75, 2018, doi: 10.1109/MCI.2018.2840738.
[30] S. M. Lundberg and S. Lee, “A Unified Approach to Interpreting Model Predictions,” in 31st Conference on Neural Information Processing Systems (NIPS 2017), 2017, no. Section 2, pp. 1–10. doi: 10.48550/arXiv.1705.07874.
[31] M. T. Ribeiro, “‘ Why Should I Trust You ?’ Explaining the Predictions of Any Classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2026, no. March, pp. 1135–1144. doi: 10.1145/2939672.2939778.
[32] R. Guidotti, “A Survey of Methods for Explaining Black Box Models A Survey of Methods for Explaining Black Box Models,” ACM Comput. Surv., vol. 51, no. 5, pp. 1–93, 2026, doi: 10.1145/3236009.
[33] Y. Xia et al., “Sustainable service quality assessment of Chinese healthcare e-government: a multi-criteria decision framework based on SERVQUAL model and entropy-weight TOPSIS method,” Front. Digit. Heal., vol. 7, p. 1611979, 2025, doi: 10.3389/fdgth.2025.1611979.
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