Applying Text Mining to Understand Customer Perception of Mobile Banking App

Dey, Mouri; Islam, Md. Zahedul; Rana, Tarek

doi:10.1007/978-981-19-4460-4_14

Cited by 2 publications

(1 citation statement)

References 54 publications

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“…The advent of the big data era necessitates the shift from conventional data analysis methods to sophisticated big data analytics capable of handling diverse datasets, both structured and unstructured, derived from a multitude of sources. Dey et al, (2023) proposes a novel methodology for the analysis of unstructured data, specifically focusing on online customer reviews of mobile banking applications, to glean insights into customer perceptions. Utilizing text mining techniques, the study encompasses the extraction and pre-processing of review data, sentiment analysis of individual reviews, and a comprehensive understanding of customer evaluations.…”

Section: Related Workmentioning

confidence: 99%

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

Balcıoğlu

2024

Dumlupınar Üniversitesi Sosyal Bilimler Dergisi

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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.

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Section: Related Workmentioning

confidence: 99%