Kurumsal İtibarın Metin ve Duygu Analiziyle Ölçülmesi: Türkiye’deki Emlak Mobil Uygulamaları Üzerine Bir İnceleme

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Şahin V., Alkan O., Özenoğlu Y. E., Şahin S., Yavuz U.

Turkish Studies - Economics, Finance, Politics , cilt.21, sa.3, ss.5875-5930, 2026 (TRDizin)

Özet

This study aims to reveal the impact of widely used real estate mobile applications in Turkey (Sahibinden, Emlakjet, Hepsiemlak) on corporate reputation by analyzing user reviews through text mining and sentiment analysis techniques. With the process of digitalization, corporate reputation management has evolved from traditional media channels toward online platforms, and mobile applications have ceased to be merely intermediaries for product and service delivery, transforming into strategic tools that shape customer experience and directly influence corporate image. Corporate reputation is regarded as a holistic reflection of the quality of relationships with stakeholders, communication strategies, service quality, and past performance, and it plays a decisive role in customer loyalty, brand commitment, and investor confidence. Within this framework, user reviews of the relevant applications obtained from the Google Play Store and the App Store were analyzed, and the distributions of positive, negative, and neutral sentiments were identified; the relationships between these distributions and reputation dimensions such as reliability, perceived quality, transparency, and customer support were then evaluated. The findings indicate that, compared to the other applications, Hepsiemlak exhibits a higher rate of positive sentiment and overall generates a more favorable user perception. Conversely, various problem areas that adversely affect user satisfaction were identified for the Sahibinden and Emlakjet applications, indicating the need for improvements in these domains. The text mining and sentiment analysis results provide a multidimensional perspective on how user experiences are reflected in perceptions of corporate reputation in the digital environment and offer data-driven strategic implications for application developers. The theoretical contribution of the study lies in its systematic and holistic treatment of mobile application user reviews within the context of reputation management. However, the fact that the analysis is restricted to reviews from specific platforms and that automated sentiment classification processes may not fully capture contextual nuances in all cases constitutes key limitations of the study; future research is therefore encouraged to address these limitations by employing larger data sets and more advanced analytical methods.