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Metin madenciliği yöntemleri ile dijital bankacılık uygulamalarına yönelik müşteri yorumlarının analizi

Yıl 2024, Cilt: 14 Sayı: 1, 45 - 60, 15.03.2024
https://doi.org/10.17714/gumusfenbil.1361431

Öz

Günümüz dijital dünyasında önemli bir konfor alanı sağlayan sanal hizmetler, insanların çoğunluğu tarafından kullanılmaktadır. Bu anlamda Dijital bankacılık da en çok faydalanılan çevrimiçi finans hizmetlerindendir. Bu araştırmada da, banka müşterileri tarafından oldukça yüksek oranda kullanılan dijital bankacılık hizmetleri ile yapmış oldukları yorumlardan oluşan veri havuzu kullanılarak metin madenciliği yöntemleri ile analiz amaçlanmıştır. Çalışmada, Türkiye Bankalar Birliği verileri ışığında en çok kullanılan 10 adet özel banka ve 3 adet devlet bankası ile toplamda 13 adet bankaya ait dijital bankacılık verileri anakütleyi oluşturmaktadır. Veriler, Ocak 2020 ile Ağustos 2022 tarihleri arası baz alınarak elde edilmiştir. Toplamda 1.200.000-1.250.000 arasında ham veri ilgili bankaların yorumlanabildiği sosyal medya platformlarından elde edilmiştir. Bankalar tek tek incelenmiş olup; kelime yoğunlukları hakkında analizler uygulanmış, wordcloud veri görselleri oluşturulmuş ve yine tek tek duygu analizleri ile bankalara bakış açısı ölçümlenmiştir. Çalışmanın sonucunda, banka müşterileri tarafından en çok; dijital uygulamaların kolaylığı, kullanışlılığı, hizmet ücretleri yorumlanmıştır. Dolayısıyla özel bankalar ile kamu bankalarına ait dijital hizmetlerin çok fazla farklılık göstermediği ancak özel bankalara ait dijital hizmetlerin kullanışlılık ve kendini yenileme anlamında daha verimli olduğu anlaşılmıştır. Yapılan analizler sonucunda bankalara, dijital bankacılık hizmetleri açısından müşteri memnuniyeti ve kaliteli hizmet sunumu kapsamında farklı önerilerde bulunulmuştur.

Kaynakça

  • Al-Hashedi, A., Al-Fuhaidi, B., Mohsen, A. M., Ali, Y., Gamal Al-Kaf, H. A., Al-Sorori, W., & Maqtary, N. (2022). Ensemble classifiers for Arabic sentiment analysis of social network (Twitter data) towards COVID-19-related conspiracy theories. Applied Computational Intelligence and Soft Computing, 2022, 1-10. https://doi.org/10.1155/2022/6614730
  • Andrian, B., Simanungkalit, T., Budi, I., & Wicaksono, A. F. (2022). Sentiment analysis on customer satisfaction of digital banking in Indonesia. International Journal of Advanced Computer Science and Applications, 13(3). https://doi.org/10.14569/IJACSA.2022.0130356
  • Chang, I. C., Yu, T. K., Chang, Y. J., & Yu, T. Y. (2021). Applying text mining, clustering analysis, and latent dirichlet allocation techniques for topic classification of environmental education journals. Sustainability, 13(19), 10856. https://doi.org/10.3390/su131910856
  • Chintalapudi, N., Battineni, G., & Amenta, F. (2021). Sentimental analysis of COVID-19 tweets using deep learning models. Infectious Disease Reports, 13(2), 329-339. https://doi.org/10.3390/idr13020032
  • Coelho, F. & Easingwood, C. (2003). Multiple channel structures in financial services: a framework. Journal of Financial Services Marketing, 8(1), 22-34. https://doi.org/10.1057/palgrave.fsm.4770104
  • Danneman, N., & Heimann, R. (2014). Social media mining with R. Packt publishing ltd.
  • Desai, R. (2019, December 26). Top 10 python libraries for data science. https://towardsdatascience.com/top-10-python-libraries-for-data-science-cd82294ec266
  • Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press, 35-46.
  • Gonzalez, G. H., Tahsin, T., Goodale, B. C., Greene, A. C., & Greene, C. S. (2016). Recent advances and emerging applications in text and data mining for biomedical discovery. Briefings in Bioinformatics, 17(1), 33-42. https://doi.org/10.1093/bib/bbv087
  • Gupta, V. & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of Emerging Technologies in Web Intelligence, 1(1), 60-76.
  • Hassani, H., Beneki, C., Unger, S., Mazinani, M. T., & Yeganegi, M. R. (2020). Text mining in big data analytics. Big Data and Cognitive Computing, 4(1), 1. https://doi.org/10.3390/bdcc4010001
  • Jadhav, A., Kaur, M., & Akter, F. (2022). Evolution of software development effort and cost estimation techniques: five decades study using automated text mining approach. Mathematical Problems in Engineering, 2022, 1-17. https://doi.org/10.1155/2022/5782587
  • Jonsson, S. & Gunnarsson, C. (2005). Internet technology to achieve supply chain performance. Business Process Management Journal, 11(4), 403-417. https://doi.org/10.1108/14637150510609426
  • Laukkanen, T. (2014). Internet vs mobile banking: comparing customer value perceptions. Business Process Management Journal, 788. https://doi.org/10.1108/14637150710834550
  • Onashabay, N. (2021). Effects of COVID-19 pandemic on the key profitability factors of digital challenger banks. Starling Bank case study [Doctoral Dissertation, Central European University].
  • Ogunleye, B. O. (2021). Statistical learning approaches to sentiment analysis in the Nigerian banking context [Doctoral dissertation, Sheffield Hallam University].
  • Mazinani, M. T., Hassani, H., & Raei, R. (2022). A comprehensive review of stock price prediction using text mining. Advances in Decision Sciences, 26(2), 1-36.
  • Miner, G., Elder IV, J., Fast, A., Hill, T., Nisbet, R., & Delen, D. (2012). Practical text mining and statistical analysis for non-structured text data applications. Academic Press.
  • Mustaqim, T., Umam, K., & Muslim, M. A. (2020). Twitter text mining for sentiment analysis on government’s response to forest fires with vader lexicon polarity detection and k-nearest neighbor algorithm. In Journal of Physics: Conference Series, 1567(3), 032024. IOP Publishing. https://doi:10.1088/1742-6596/1567/3/032024
  • Ngo, V. M., Van Nguyen, P., Nguyen, H. H., Tram, H. X. T., & Hoang, L. C. (2023). Governance and monetary policy impacts on public acceptance of CBDC adoption. Research in International Business and Finance, 64, 101865. https://doi.org/10.1016/j.ribaf.2022.101865
  • Park, S., Lee, J., & Park, Y. (2022). Analysis of residential satisfaction changes by the land bank program using text mining. Sustainability, 14(18), 11707. https://doi.org/10.3390/Su141811707
  • Saini, S., & Mohan Pandey, H. (2015). Review on web content mining techniques. International Journal of Computer Applications, 118(18), 33–36. https://doi.org/10.5120/20848-3536
  • Sayın Okatan, B. (2023). Investigating customer reviews on digital banking applications through text mining methods, [Doctoral dissertation, Gümüşhane University Graduate Institute].
  • Sharma, R., Nigam, S., & Jain, R. (2014). Opinion mining of movie reviews at document level. arXiv preprint arXiv:1408.3829. https://doi.org/10.48550/arXiv.1408.3829
  • Vijayarani, S., Ilamathi, M. J., & Nithya, M. (2015). Preprocessing techniques for text mining-an overview. International Journal of Computer Science & Communication networks, 5(1), 7-16.
  • Villalon, J., & Calvo, R. A. (2009). Concept extraction from student essays, towards concept map mining. In 2009 ninth IEEE International Conference on Advanced Learning Technologies, 221–225. IEEE. https://doi: 10.1109/ICALT.2009.215
  • Zhai, C., & Massung, S. (2016). Text data management and analysis: a practical introduction to information retrieval and text mining. Morgan & Claypool Publishers. https://doi.org/10.1145/2915031

Analysis of customer reviews for digital banking applications with text mining methods

Yıl 2024, Cilt: 14 Sayı: 1, 45 - 60, 15.03.2024
https://doi.org/10.17714/gumusfenbil.1361431

Öz

Virtual services, which provide an important comfort area in today's digital world, are used by the majority of people. Accordingly, digital banking is one of the most used online financial services. In this research, it was aimed to analyze the digital banking services used by bank customers at a high rate and by using text mining methods using a data pool consisting of their comments. In the study, in the light of the data of the Banks Association of Turkey, the digital banking data of the 10 most used private banks and 3 state banks and a total of 13 banks constitute the population. The data covers the period from January 2020 to August 2022.In total, between 1,200,000-1,250,000 raw data were obtained from social media platforms where the relevant banks could be interpreted. Banks were examined one by one; Analyzes about word density were applied, wordcloud data visuals were created, and the perspective on banks was measured with individual sentiment analyses. As a result of the study, the most frequently cited by bank customers are The ease, usefulness, and service fees of digital applications are interpreted. Therefore, it has been understood that the digital services of private banks and public banks do not differ much, but the digital services of private banks are more efficient in terms of usefulness and self-renewal. As a result of the analysis, different suggestions were made to banks within the scope of customer satisfaction and quality service delivery in terms of digital banking services.

Kaynakça

  • Al-Hashedi, A., Al-Fuhaidi, B., Mohsen, A. M., Ali, Y., Gamal Al-Kaf, H. A., Al-Sorori, W., & Maqtary, N. (2022). Ensemble classifiers for Arabic sentiment analysis of social network (Twitter data) towards COVID-19-related conspiracy theories. Applied Computational Intelligence and Soft Computing, 2022, 1-10. https://doi.org/10.1155/2022/6614730
  • Andrian, B., Simanungkalit, T., Budi, I., & Wicaksono, A. F. (2022). Sentiment analysis on customer satisfaction of digital banking in Indonesia. International Journal of Advanced Computer Science and Applications, 13(3). https://doi.org/10.14569/IJACSA.2022.0130356
  • Chang, I. C., Yu, T. K., Chang, Y. J., & Yu, T. Y. (2021). Applying text mining, clustering analysis, and latent dirichlet allocation techniques for topic classification of environmental education journals. Sustainability, 13(19), 10856. https://doi.org/10.3390/su131910856
  • Chintalapudi, N., Battineni, G., & Amenta, F. (2021). Sentimental analysis of COVID-19 tweets using deep learning models. Infectious Disease Reports, 13(2), 329-339. https://doi.org/10.3390/idr13020032
  • Coelho, F. & Easingwood, C. (2003). Multiple channel structures in financial services: a framework. Journal of Financial Services Marketing, 8(1), 22-34. https://doi.org/10.1057/palgrave.fsm.4770104
  • Danneman, N., & Heimann, R. (2014). Social media mining with R. Packt publishing ltd.
  • Desai, R. (2019, December 26). Top 10 python libraries for data science. https://towardsdatascience.com/top-10-python-libraries-for-data-science-cd82294ec266
  • Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press, 35-46.
  • Gonzalez, G. H., Tahsin, T., Goodale, B. C., Greene, A. C., & Greene, C. S. (2016). Recent advances and emerging applications in text and data mining for biomedical discovery. Briefings in Bioinformatics, 17(1), 33-42. https://doi.org/10.1093/bib/bbv087
  • Gupta, V. & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of Emerging Technologies in Web Intelligence, 1(1), 60-76.
  • Hassani, H., Beneki, C., Unger, S., Mazinani, M. T., & Yeganegi, M. R. (2020). Text mining in big data analytics. Big Data and Cognitive Computing, 4(1), 1. https://doi.org/10.3390/bdcc4010001
  • Jadhav, A., Kaur, M., & Akter, F. (2022). Evolution of software development effort and cost estimation techniques: five decades study using automated text mining approach. Mathematical Problems in Engineering, 2022, 1-17. https://doi.org/10.1155/2022/5782587
  • Jonsson, S. & Gunnarsson, C. (2005). Internet technology to achieve supply chain performance. Business Process Management Journal, 11(4), 403-417. https://doi.org/10.1108/14637150510609426
  • Laukkanen, T. (2014). Internet vs mobile banking: comparing customer value perceptions. Business Process Management Journal, 788. https://doi.org/10.1108/14637150710834550
  • Onashabay, N. (2021). Effects of COVID-19 pandemic on the key profitability factors of digital challenger banks. Starling Bank case study [Doctoral Dissertation, Central European University].
  • Ogunleye, B. O. (2021). Statistical learning approaches to sentiment analysis in the Nigerian banking context [Doctoral dissertation, Sheffield Hallam University].
  • Mazinani, M. T., Hassani, H., & Raei, R. (2022). A comprehensive review of stock price prediction using text mining. Advances in Decision Sciences, 26(2), 1-36.
  • Miner, G., Elder IV, J., Fast, A., Hill, T., Nisbet, R., & Delen, D. (2012). Practical text mining and statistical analysis for non-structured text data applications. Academic Press.
  • Mustaqim, T., Umam, K., & Muslim, M. A. (2020). Twitter text mining for sentiment analysis on government’s response to forest fires with vader lexicon polarity detection and k-nearest neighbor algorithm. In Journal of Physics: Conference Series, 1567(3), 032024. IOP Publishing. https://doi:10.1088/1742-6596/1567/3/032024
  • Ngo, V. M., Van Nguyen, P., Nguyen, H. H., Tram, H. X. T., & Hoang, L. C. (2023). Governance and monetary policy impacts on public acceptance of CBDC adoption. Research in International Business and Finance, 64, 101865. https://doi.org/10.1016/j.ribaf.2022.101865
  • Park, S., Lee, J., & Park, Y. (2022). Analysis of residential satisfaction changes by the land bank program using text mining. Sustainability, 14(18), 11707. https://doi.org/10.3390/Su141811707
  • Saini, S., & Mohan Pandey, H. (2015). Review on web content mining techniques. International Journal of Computer Applications, 118(18), 33–36. https://doi.org/10.5120/20848-3536
  • Sayın Okatan, B. (2023). Investigating customer reviews on digital banking applications through text mining methods, [Doctoral dissertation, Gümüşhane University Graduate Institute].
  • Sharma, R., Nigam, S., & Jain, R. (2014). Opinion mining of movie reviews at document level. arXiv preprint arXiv:1408.3829. https://doi.org/10.48550/arXiv.1408.3829
  • Vijayarani, S., Ilamathi, M. J., & Nithya, M. (2015). Preprocessing techniques for text mining-an overview. International Journal of Computer Science & Communication networks, 5(1), 7-16.
  • Villalon, J., & Calvo, R. A. (2009). Concept extraction from student essays, towards concept map mining. In 2009 ninth IEEE International Conference on Advanced Learning Technologies, 221–225. IEEE. https://doi: 10.1109/ICALT.2009.215
  • Zhai, C., & Massung, S. (2016). Text data management and analysis: a practical introduction to information retrieval and text mining. Morgan & Claypool Publishers. https://doi.org/10.1145/2915031
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yönetim Bilişim Sistemleri
Bölüm Makaleler
Yazarlar

Burcu Okatan 0000-0002-2911-4568

Handan Çam 0000-0003-0982-2919

Yayımlanma Tarihi 15 Mart 2024
Gönderilme Tarihi 16 Eylül 2023
Kabul Tarihi 23 Ekim 2023
Yayımlandığı Sayı Yıl 2024 Cilt: 14 Sayı: 1

Kaynak Göster

APA Okatan, B., & Çam, H. (2024). Analysis of customer reviews for digital banking applications with text mining methods. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 14(1), 45-60. https://doi.org/10.17714/gumusfenbil.1361431