In this study, the impact of mobile applications developed by the Ministry of Health of the Republic of Turkey as part of its digitalization strategy on corporate reputation is analysed by using artificial intelligence methods through user comments. Within the scope of the research, the last 300 user comments of MHRS, Hayat Eve Sığar and eNabız applications on Google Play were analysed, and sentiment analysis and text mining techniques were applied. The findings reveal that MHRS and eNabız applications are generally perceived positively by users, which has a positive impact on the corporate reputation of the Ministry of Health. 81% of MHRS users and 73% of eNabız users made positive comments about the applications. However, for the Hayat Eve Sığar application, the positive comment rate remained at 51 percent, and more technical problems were reported. This shows that the application offers complex user experiences and needs to be improved. In conclusion, it is emphasized that the mobile applications of the Ministry of Health have strengthened its corporate reputation in general, but user satisfaction and sustainability of technical performance are critical to maintaining this reputation.
Addiga, A., & Bagui, S. (2022). Sentiment Analysis on Twitter Data Using Term Frequency-Inverse Document Frequency. Journal of Computer and Communications, 10(8), 117–128. https://doi.org/10.4236/jcc.2022.108008
Agrawal, R., & Batra, M. (2013). A detailed study on text mining techniques. International Journal of Soft Computing and Engineering, 2(6), 118–121.
Bonta, V., Kumaresh, N., & Janardhan, N. (2019). A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis. Asian Journal of Computer Science and Technology, 8(S2), 1–6. https://doi.org/10.51983/ajcst-2019.8.s2.2037
Brynjolfsson, E., & Mcafee, A. (2017). Artificial intelligence, for real. Harvard Business Review, 1, 1–31.
Caropreso, M. F., Matwin, S., & Sebastiani, F. (2001). A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization. Text Databases and Document Management: Theory and Practice, 5478(4), 78–102.
Clark, J. H., Garrette, D., Turc, I., & Wieting, J. (2022). Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation. Transactions of the Association for Computational Linguistics, 10, 73–91. https://doi.org/10.1162/tacl\_a\_00448
Elsaid Moussa, M., Hussein Mohamed, E., & Hassan Haggag, M. (2021). Opinion mining: a hybrid framework based on lexicon and machine learning approaches. International Journal of Computers and Applications, 43(8), 786–794. https://doi.org/10.1080/1206212x.2019.1615250
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2012). A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches. IEEE Transactions on Systems, Man, And Cybernetics, Part C (Applications and Reviews), 42(4), 463–484. https://doi.org/10.1109/tsmcc.2011.2161285
Genc-Nayebi, N., & Abran, A. (2017). A systematic literature review: Opinion mining studies from mobile app store user reviews. Journal of Systems and Software, 125, 207–219. https://doi.org/10.1016/j.jss.2016.11.027
Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision (Issue CS224N).
Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267–297. https://doi.org/10.1093/pan/mps028
He, L., Yang, Z., Lin, H., & Li, Y. (2014). Drug name recognition in biomedical texts: a machine-learning-based method. Drug Discovery Today, 19(5), 610–617. https://doi.org/10.1016/j.drudis.2013.10.006
Hearst, M. A., Pedersen, E., Patil, L., Lee, E., Laskowski, P., & Franconeri, S. (2020). An Evaluation of Semantically Grouped Word Cloud Designs. IEEE Transactions on Visualization and Computer Graphics, 26(9), 2748–2761. https://doi.org/10.1109/tvcg.2019.2904683
Huang, C.-H., Yin, J., & Hou, F. (2011). A Text Similarity Measurement Combining Word Semantic Information with TF-IDF Method: A Text Similarity Measurement Combining Word Semantic Information with TF-IDF Method. Chinese Journal of Computers, 34(5), 856–864. https://doi.org/10.3724/sp.j.1016.2011.00856
Jivani, A. G. (2011). A comparative study of stemming algorithms. Int. J. Comp. Tech. Appl., 2(6), 1930–1938.
Kang, D., & Park, Y. (2014). Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach. Expert Systems with Applications, 41(4), 1041–1050. https://doi.org/10.1016/j.eswa.2013.07.101
Kayakuş, M., & Yiğit Açıkgöz, F. (2023). Twitter'da Makine Öğrenmesi Yöntemleriyle Sahte Haber Tespiti. Abant Sosyal Bilimler Dergisi, 23(2), 1017–1027. https://doi.org/10.11616/asbi.1266179
Latif, S., Rana, R., Qadir, J., Ali, A., Imran, M. A., & Younis, M. S. (2017). Mobile Health in the Developing World: Review of Literature and Lessons From a Case Study. IEEE Access, 5, 11540–11556. https://doi.org/10.1109/access.2017.2710800
Loke, R., & Pathak, S. (2023). Decision Support System for Corporate Reputation Based Social Media Listening Using a Cross-Source Sentiment Analysis Engine. Proceedings of the 12th International Conference on Data Science, Technology and Applications, 559–567. https://doi.org/10.5220/0012136400003541
McCuiston, V. E., & DeLucenay, A. (2010). Organization Development Quality Improvement Process: Progress Energy's Continuous Business Excellence Initiative. Journal of Business Case Studies (JBCS), 6(6). https://doi.org/10.19030/jbcs.v6i6.255
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
Mostafa, M. M. (2013). More than words: Social networks' text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241–4251. https://doi.org/10.1016/j.eswa.2013.01.019
Nguyen, B.-H., & Huynh, V.-N. (2022). Textual analysis and corporate bankruptcy: A financial dictionary-based sentiment approach. Journal of the Operational Research Society, 73(1), 102–121. https://doi.org/10.1080/01605682.2020.1784049
Nguyen, N., & Leblanc, G. (2001). Corporate image and corporate reputation in customers' retention decisions in services. Journal of Retailing and Consumer Services, 8(4), 227–236. https://doi.org/10.1016/s0969-6989(00)00029-1
O'Brien, H. L., & Toms, E. G. (2008). What is user engagement? A conceptual framework for defining user engagement with technology. Journal of the American Society for Information Science and Technology, 59(6), 938–955. https://doi.org/10.1002/asi.20801
Ogada, K., Mwangi, W., & Cheruiyot, W. (2015). N-gram based text categorization method for improved data mining. Journal of Information Engineering and Applications, 5(8), 35–43.
Pandey, M., Williams, R., Jindal, N., & Batra, A. (2019). Sentiment analysis using lexicon based approach. IITM Journal of Management and IT, 10(1), 68–76.
Pejić Bach, M., Krstić, Ž., Seljan, S., & Turulja, L. (2019). Text Mining for Big Data Analysis in Financial Sector: A Literature Review. Sustainability, 11(5), 1277. https://doi.org/10.3390/su11051277
Peng, Z., & Wan, Y. (2023). Generating business intelligence through automated textual analysis: measuring corporate image with online information. Chinese Management Studies, 17(3), 545–572. https://doi.org/10.1108/cms-07-2021-0318
Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51–59. https://doi.org/10.1089/big.2013.1508
Qiang, C. Z., Yamamichi, M., Hausman, V., Altman, D., & Unit, I. (2011). Mobile Applications for the Health Sector_2011. The World Bank.
Sial, A. H., Rashdi, S. Y. S., & Khan, A. H. (2021). International Journal of Advanced Trends in Computer Science and Engineering, 10(1), 277–281. https://doi.org/10.30534/ijatcse/2021/391012021
Umadevi, M. (2020). Document comparison based on TF-IDF metric. International Research Journal of Engineering and Technology, 7(2), 1546–1550.
Wan Min, W. N. S., & Zulkarnain, N. Z. (2020). Comparative Evaluation of Lexicons in Performing Sentiment Analysis. Journal of Advanced Computing Technology and Application, 2(1), 1–8. https://jacta.utem.edu.my/jacta/article/view/5207
Waskom, M. (2021). seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021. https://doi.org/10.21105/joss.03021
Weichbroth, P., & Baj-Rogowska, A. (2019). Do online reviews reveal mobile application usability and user experience? The case of WhatsApp. Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, 18, 747–754. https://doi.org/10.15439/2019f289
Zhou, H., & Slater, G. W. (2003). A metric to search for relevant words. Physica A: Statistical Mechanics and Its Applications, 329(1–2), 309–327. https://doi.org/10.1016/s0378-4371(03)00625-3
alphanumeric journal has been publishing as "International Peer-Reviewed
Journal" every six months since 2013. alphanumeric serves as a vehicle for researchers and
practitioners in the field of quantitative methods, and is enabling a process of sharing in all
fields related to the operations research, statistics, econometrics and management informations
systems in order to enhance the quality on a globe scale.