machine learning; sentiment analysis; emotional features; food delivery service apps; user evaluations; thumbs-up behavior

Al-Subaihin, A. A., Sarro, F., Black, S., et al. (2019). App Store Effects on Software Engineering Practices. IEEE Transactions on Software Engineering, 47(2), 300–319. https://doi.org/10.1109/tse.2019.2891715 Annaraud, K., & Berezina, K. (2020). Predicting satisfaction and intentions to use online food delivery: What really makes a difference? Journal of Foodservice Business Research, 23(4), 305–323. https://doi.org/10.1080/15378020.2020.1768039 Ashton-James, C. E., & Ashkanasy, N. M. (2008). Chapter 1 Affective events theory: A strategic perspective. In: Zerbe, W. J., Härtel, C. E. J., & Ashkanasy, N. M. (editors). Emotions, Ethics and Decision-Making. Emerald Group Publishing Limited. Askalidis, G., & Malthouse, E. C. (2016). The Value of Online Customer Reviews. In: Proceedings of the 10th ACM Conference on Recommender Systems. Chandrasekhar, N., Gupta, S., & Nanda, N. (2019). Food Delivery Services and Customer Preference: A Comparative Analysis. Journal of Foodservice Business Research, 22(4), 375–386. https://doi.org/10.1080/15378020.2019.1626208 Chen, C.-F. (2023). Investigating the Effects of Job Stress on the Distraction and Risky Driving Behaviors of Food Delivery Motorcycle Riders. Safety and Health at Work, 14(2), 207–214. https://doi.org/10.1016/j.shaw.2023.03.004 Chuah, S. H.-W., & Yu, J. (2021). The future of service: The power of emotion in human-robot interaction. Journal of Retailing and Consumer Services, 61, 102551. https://doi.org/10.1016/j.jretconser.2021.102551 Coombs, W. T., & Holladay, S. J. (2007). An exploratory study of stakeholder emotions: Affect and crises. In: Research on Emotion in Organizations. Emerald Group Publishing Limited. Dąbrowski, J., Letier, E., Perini, A., et al. (2022). Analysing app reviews for software engineering: a systematic literature review. Empirical Software Engineering, 27(2). https://doi.org/10.1007/s10664-021-10065-7 Dąbrowski, J., Letier, E., Perini, A., et al. (2023). Mining and searching app reviews for requirements engineering: Evaluation and replication studies. Information Systems, 114, 102181. https://doi.org/10.1016/j.is.2023.102181 Davidow, M. (2000). The Bottom Line Impact of Organizational Responses to Customer Complaints. Journal of Hospitality & Tourism Research, 24(4), 473–490. https://doi.org/10.1177/109634800002400404 Diekson, Z. A., Prakoso, M. R. B., Putra, M. S. Q., et al. (2023). Sentiment analysis for customer review: Case study of Traveloka. Procedia Computer Science, 216, 682–690. https://doi.org/10.1016/j.procs.2022.12.184 Elahi, M., Khosh Kholgh, D., Kiarostami, M. S., et al. (2023). Hybrid recommendation by incorporating the sentiment of product reviews. Information Sciences, 625, 738–756. https://doi.org/10.1016/j.ins.2023.01.051 Fernandes, S., Panda, R., Venkatesh, V. G., et al. (2022). Measuring the impact of online reviews on consumer purchase decisions – A scale development study. Journal of Retailing and Consumer Services, 68, 103066. https://doi.org/10.1016/j.jretconser.2022.103066 Ghasemaghaei, M., Eslami, S. P., Deal, K., et al. (2018). Reviews’ length and sentiment as correlates of online reviews’ ratings. Internet Research, 28(3), 544–563. https://doi.org/10.1108/intr-12-2016-0394 Hossain, M. S., & Rahman, M. F. (2023a). Customer Sentiment Analysis and Prediction of Insurance Products’ Reviews Using Machine Learning Approaches. FIIB Business Review, 12(4), 386–402. https://doi.org/10.1177/23197145221115793 Hossain, M. S., & Rahman, M. F. (2023b). Detection of readers’ emotional aspects and thumbs-up empathy reactions towards reviews of online travel agency apps. Journal of Hospitality and Tourism Insights, 7(1), 142–171. https://doi.org/10.1108/jhti-10-2022-0487 Hossain, M. S., & Rahman, M. F. (2022). Detection of potential customers’ empathy behavior towards customers’ reviews. Journal of Retailing and Consumer Services, 65, 102881. https://doi.org/10.1016/j.jretconser.2021.102881 Hu, N., Koh, N. S., & Reddy, S. K. (2014). Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decision Support Systems, 57, 42–53. https://doi.org/10.1016/j.dss.2013.07.009 Islam, Md. J., Datta, R., & Iqbal, A. (2023). Actual rating calculation of the zoom cloud meetings app using user reviews on google play store with sentiment annotation of BERT and hybridization of RNN and LSTM. Expert Systems with Applications, 223, 119919. https://doi.org/10.1016/j.eswa.2023.119919 Jiménez-Zafra, S. M., Martín-Valdivia, M. T., Maks, I., & Izquierdo, R. (2017). Analysis of patient satisfaction in Dutch and Spanish online reviews. Procesamiento del Lenguaje Natural, 58, 101-108. Kaur, M., Verma, R., & Otoo, F. N. K. (2021). Emotions in leader’s crisis communication: Twitter sentiment analysis during COVID-19 outbreak. Journal of Human Behavior in the Social Environment, 31(1–4), 362–372. https://doi.org/10.1080/10911359.2020.1829239 Kesner, L., & Horáček, J. (2017). Empathy-Related Responses to Depicted People in Art Works. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.00228 Lai, X., Wang, F., & Wang, X. (2021). Asymmetric relationship between customer sentiment and online hotel ratings: the moderating effects of review characteristics. International Journal of Contemporary Hospitality Management, 33(6), 2137–2156. https://doi.org/10.1108/ijchm-07-2020-0708 Leon, R. D. (2019). Hotel’s online reviews and ratings: a cross-cultural approach. International Journal of Contemporary Hospitality Management, 31(5), 2054–2073. https://doi.org/10.1108/ijchm-05-2018-0413 Leung, X. Y., & Yang, Y. (2020). Are all five points equal? Scaling heterogeneity in hotel online ratings. International Journal of Hospitality Management, 88, 102539. https://doi.org/10.1016/j.ijhm.2020.102539 Li, H., Yu, B. X. B., Li, G., et al. (2023). Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews. Tourism Management, 96, 104707. https://doi.org/10.1016/j.tourman.2022.104707 Li, H., Liu, H., & Zhang, Z. (2020). Online persuasion of review emotional intensity: A text mining analysis of restaurant reviews. International Journal of Hospitality Management, 89, 102558. https://doi.org/10.1016/j.ijhm.2020.102558 Lin, X.-M., Ho, C.-H., Xia, L.-T., et al. (2021). Sentiment analysis of low-carbon travel APP user comments based on deep learning. Sustainable Energy Technologies and Assessments, 44, 101014. https://doi.org/10.1016/j.seta.2021.101014 Liu, Y., & Li, S. (2023). An economic analysis of on-demand food delivery platforms: Impacts of regulations and integration with ride-sourcing platforms. Transportation Research Part E: Logistics and Transportation Review, 171, 103019. https://doi.org/10.1016/j.tre.2023.103019 Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140–151. https://doi.org/10.1016/j.tourman.2014.09.020 Longobardi, C., Borello, L., Thornberg, R., et al. (2019). Empathy and defending behaviours in school bullying: The mediating role of motivation to defend victims. British Journal of Educational Psychology, 90(2), 473–486. Portico. https://doi.org/10.1111/bjep.12289 Maimaiti, M., Zhao, X., Jia, M., et al. (2018). How we eat determines what we become: opportunities and challenges brought by food delivery industry in a changing world in China. European Journal of Clinical Nutrition, 72(9), 1282–1286. https://doi.org/10.1038/s41430-018-0191-1 Mariani, M. M., & Borghi, M. (2018). Effects of the Booking.com rating system: Bringing hotel class into the picture. Tourism Management, 66, 47–52. https://doi.org/10.1016/j.tourman.2017.11.006 McGlohon, M., Glance, N., & Reiter, Z. (2010). Star Quality: Aggregating Reviews to Rank Products and Merchants. Proceedings of the International AAAI Conference on Web and Social Media, 4(1), 114–121. https://doi.org/10.1609/icwsm.v4i1.14019 Mellinas, J. P., María-Dolores, S. M. M., & García, J. J. B. (2016). Effects of the Booking.com scoring system. Tourism Management, 57, 80-83. https://doi.org/10.1016/j.tourman.2016.05.015 Ossai, C. I., & Wickramasinghe, N. (2023). Sentiments prediction and thematic analysis for diabetes mobile apps using Embedded Deep Neural Networks and Latent Dirichlet Allocation. Artificial Intelligence in Medicine, 138, 102509. https://doi.org/10.1016/j.artmed.2023.102509 Pandey, S., Chawla, D., & Puri, S. (2022). Food delivery apps (FDAs) in Asia: an exploratory study across India and the Philippines. British Food Journal, 124(3), 657–678. https://doi.org/10.1108/bfj-01-2020-0074 Parboteeah, D. V., Valacich, J. S., & Wells, J. D. (2009). The Influence of Website Characteristics on a Consumer’s Urge to Buy Impulsively. Information Systems Research, 20(1), 60–78. https://doi.org/10.1287/isre.1070.0157 Pashchenko, Y., Rahman, M. F., Hossain, M. S., et al. (2022). Emotional and the normative aspects of customers’ reviews. Journal of Retailing and Consumer Services, 68, 103011. https://doi.org/10.1016/j.jretconser.2022.103011 Peterson, R. A., & Merino, M. C. (2003). Consumer information search behavior and the internet. Psychology & Marketing, 20(2), 99–121. Portico. https://doi.org/10.1002/mar.10062 Plutchik, R. (1980). A general psychoevolutionary theory of emotion. Theories of Emotion, 3–33. https://doi.org/10.1016/b978-0-12-558701-3.50007-7 Puh, K., & Bagić Babac, M. (2022). Predicting sentiment and rating of tourist reviews using machine learning. Journal of Hospitality and Tourism Insights, 6(3), 1188–1204. https://doi.org/10.1108/jhti-02-2022-0078 Qi, H., & Li, F. (2021). Travelers’ emotional experiences during the COVID-19 outbreak: The development of a conceptual model. Journal of Hospitality and Tourism Management, 47, 389–397. https://doi.org/10.1016/j.jhtm.2021.04.013 Rajeswari, B., Madhavan, S., Venkatesakumar, R., et al. (2020). Sentiment analysis of consumer reviews – a comparison of organic and regular food products usage. Rajagiri Management Journal, 14(2), 155–167. https://doi.org/10.1108/ramj-05-2020-0022 Ramachandran, R., Sudhir, S., & Unnithan, A. B. (2021). Exploring the relationship between emotionality and product star ratings in online reviews. IIMB Management Review, 33(4), 299–308. https://doi.org/10.1016/j.iimb.2021.12.002 Ratnasari, R. T., Gunawan, S., Mawardi, I., & Kirana, K. C. (2020). Emotional experience on behavioral intention for halal tourism. Journal of Islamic Marketing, 12(4), 864–881. https://doi.org/10.1108/JIMA-12-2019-0256 Rita, P., Ramos, R., Borges-Tiago, M. T., et al. (2022). Impact of the rating system on sentiment and tone of voice: A Booking.com and TripAdvisor comparison study. International Journal of Hospitality Management, 104, 103245. https://doi.org/10.1016/j.ijhm.2022.103245 Sánchez-Franco, M. J., Arenas-Márquez, F. J., & Alonso-Dos-Santos, M. (2021). Using structural topic modelling to predict users’ sentiment towards intelligent personal agents. An application for Amazon’s echo and Google Home. Journal of Retailing and Consumer Services, 63, 102658. https://doi.org/10.1016/j.jretconser.2021.102658 Sarkar, A. R., & Ahmad, S. (2021). A new approach to expert reviewer detection and product rating derivation from online experiential product reviews. Heliyon, 7(7), e07409. https://doi.org/10.1016/j.heliyon.2021.e07409 Sayfuddin, A., & Chen, Y. (2021). The signaling and reputational effects of customer ratings on hotel revenues: Evidence from TripAdvisor. International Journal of Hospitality Management, 99, 103065. https://doi.org/10.1016/j.ijhm.2021.103065 Su, D. N., Nguyen-Phuoc, D. Q., Duong, T. H., et al. (2022). How does quality of mobile food delivery services influence customer loyalty? Gronroos’s service quality perspective. International Journal of Contemporary Hospitality Management, 34(11), 4178–4205. https://doi.org/10.1108/ijchm-08-2021-1039 Suhartanto, D., Helmi Ali, M., Tan, K. H., et al. (2019). Loyalty toward online food delivery service: the role of e-service quality and food quality. Journal of Foodservice Business Research, 22(1), 81–97. https://doi.org/10.1080/15378020.2018.1546076 Trivedi, S. K., & Singh, A. (2021). Twitter sentiment analysis of app based online food delivery companies. Global Knowledge, Memory and Communication, 70(8/9), 891–910. https://doi.org/10.1108/gkmc-04-2020-0056 Umasuthan, H., Park, O.-J., & Ryu, J.-H. (2017). Influence of empathy on hotel guests’ emotional service experience. Journal of Services Marketing, 31(6), 618–635. https://doi.org/10.1108/JSM-06-2016-0220 https://doi.org/10.1108/JSM-06-2016-0220 Wang, X., Zhao, F., Tian, X., et al. (2022). How online food delivery platforms contributed to the resilience of the urban food system in China during the COVID-19 pandemic. Global Food Security, 35, 100658. https://doi.org/10.1016/j.gfs.2022.100658 Weiss, H. M., & Cropanzano, R. (1996). Affective Events Theory: A theoretical discussion of the structure, causes and consequences of affective experiences at work. In: Staw, B. M. & Cummings, L. L. (editors). Research in Organizational Behavior: An Annual Series of Analytical Essays and Critical Reviews. Elsevier Science/JAI Press. Yaiprasert, C., & Hidayanto, A. N. (2023). AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business. Intelligent Systems with Applications, 18, 200235. https://doi.org/10.1016/j.iswa.2023.200235 Zhang, Z., Liang, S., Li, H., et al. (2019). Booking now or later: Do online peer reviews matter? International Journal of Hospitality Management, 77, 147–158. https://doi.org/10.1016/j.ijhm.2018.06.024 Zhao, Y., Yang, S., Narayan, V., & Zhao, Y. (2013). Modeling consumer learning from online product reviews. Marketing Science, 32(1), 153–169. https://doi.org/10.1287/mksc.1120.0755