This study proposes a methodological framework for measuring mobile banking service quality through text mining of customer-generated content. The research employs sentiment analysis techniques to evaluate service quality dimensions across three prominent Turkish private banks based on 9,547 Google Play Store reviews. Following a systematic preprocessing protocol, reviews underwent sentiment classification using a Naive Bayes algorithm, achieving 87.3% accuracy. The analysis revealed distinct patterns across five service quality dimensions: Practicality emerged as the strongest dimension (mean score: 0.75), while Sociality demonstrated the most significant deficiency (mean score: 0.32). Statistical comparison identified significant inter-bank differences, with İşCep demonstrating superior overall performance (0.67), followed by Garanti BBVA (0.65) and TEB (0.58). Network analysis of keyword co-occurrences illuminated the semantic structure of customer discourse, revealing distinctive terminological communities within dimensional frameworks. The methodology transcends traditional survey-based approaches by providing continuous, scalable quality assessment derived from authentic customer expressions. Moreover, the integration of dimensional analysis with complaint pattern identification establishes clear priorities for service enhancement initiatives. This research advances both the theoretical understanding of mobile banking service quality dimensions and the practical implementation of computational text analysis in service quality measurement frameworks.
Çelik et al. (Sat,) studied this question.