Analyzing Customer Sentiments and Trends in Turkish Mobile Banking Apps: A Text Mining Study

Abstract

This study investigates customer satisfaction with mobile banking applications in Turkey through a comprehensive text mining analysis of user-generated reviews. Drawing from a large corpus of data across ten leading Turkish banks, including Ziraat Bank, İş Bank, Garanti BBVA, Akbank, Yapı Kredi Bank, Halkbank, Vakıfbank, DenizBank, QNB Finansbank, and Turkey Şekerbank, the alignment between user ratings and sentiments is explored to uncover the nuances of customer feedback. The dataset undergoes rigorous preprocessing, sentiment analysis, trend analysis, and Latent Dirichlet Allocation (LDA) topic modeling to identify prevailing themes and factors affecting user satisfaction. The methodology involves the classification of reviews into positive, negative, and neutral sentiments and the examination of trends over time to pinpoint periods of heightened dissatisfaction. The analysis is further augmented by the application of advanced machine learning algorithms, including Random Forest, Gradient Boosting Machine, and BERT, showcasing an accuracy range between 92% and 95% in sentiment classification. The results of the topic modeling are visualized through word clouds, providing a clear depiction of the dominant themes in user feedback. Trend analysis over time identifies critical periods where negative reviews surpass positive ones, often coinciding with app updates or changes in service features. The findings highlight the necessity for continuous improvement and testing of mobile banking applications to meet customer expectations effectively.

Keywords

• Mobile banking satisfaction
• Sentiment analysis
• User experience trends
• Topic modeling
• Customer feedback analysis

Türkiye'deki Mobil Bankacılık Uygulamalarında Müşteri Duygularını ve Eğilimlerini Analiz Etme: Bir Metin Madenciliği Çalışması

Abstract

BBu çalışma, Türkiye'deki mobil bankacılık uygulamalarına yönelik müşteri memnuniyetini, kullanıcı tarafından oluşturulan yorumların kapsamlı bir metin madenciliği analiziyle araştırmaktadır. Türkiye'nin önde gelen on Bankna ait geniş bir veri kümesinden yararlanarak, kullanıcı derecelendirmeleri ve duyguları arasındaki uyumu inceleyerek müşteri geri bildirimlerinin inceliklerini ortaya çıkarmayı amaçlamaktadır. Ziraat Bankası, İş Bankası, Garanti BBVA, Akbankası, Yapı Kredi Bankası, Halkbankası, Vakıfbank, Denizbank, QNB Finansbank ve Türkiye Şekerbankası’ndan toplanan yorumları içeren veri seti, kullanıcı memnuniyetini etkileyen hâkim temaları ve faktörleri belirlemek için titiz ön işleme, duygu analizi, eğilim analizi ve Latent Dirichlet Allocation (LDA) konu modellemesine tabi tutulmuştur. Metodolojimiz, yorumların olumlu, olumsuz ve nötr duygulara sınıflandırılmasını ve zaman içinde eğilimleri inceleyerek memnuniyetsizliğin arttığı dönemleri belirleme işlemi içermektedir. Analiz, Random Forest, Gradient Boosting Machine ve BERT gibi gelişmiş makine öğrenimi algoritmalarının uygulanmasıyla daha da geliştirilmiş ve bu algoritmalar, duygu sınıflandırmasında %92 ile %95 arasında değişen bir doğruluk oranı sergilemektedir. Çalışma, verilen derecelendirme ile duyguların uyumlu olmadığı önemli bir yorum oranını ortaya çıkarmıştır ve bu durum, sadece derecelendirmelerle ifade edilmeyen kullanıcı deneyimlerindeki karmaşıklıkları göstermektedir. Zaman içinde yapılan eğilim analizi, olumsuz yorumların olumlu olanları aştığı kritik dönemleri belirlemiş, bunlar genellikle uygulama güncellemeleri veya hizmet özelliklerindeki değişikliklerle çakışmaktadır. Bulgularımız, müşteri beklentilerini etkili bir şekilde karşılamak için mobil bankacılık uygulamalarının sürekli iyileştirilmesi ve test edilmesi gerekliliğini vurgulamaktadır.

Keywords

• Mobil bankacılık tatmini
• Duygu analizi
• Kullanıcı deneyimleri
• Konu modelleme
• Müşteri deneyimi analizi

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