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Exploring Mobile Application User Experience Through Topic Modeling

Author

Listed:
  • Olivera Grljević

    (Faculty of Economics in Subotica, University of Novi Sad, 24000 Subotica, Serbia)

  • Mirjana Marić

    (Faculty of Economics in Subotica, University of Novi Sad, 24000 Subotica, Serbia)

  • Rade Božić

    (Faculty of Business Economics Bijeljina, University of East Sarajevo, 76300 Bijeljina, Bosnia and Herzegovina)

Abstract

Exploring user satisfaction and experience is the first step of software product improvement and business sustainability. The primary goal of this research is to investigate how companies can use topic modeling to understand mobile application user experience and offer methodological steps for identifying factors shaping it. SalesForce was selected for this case study as it was the most widely used CRM application in 2023. This study aims to uncover factors influencing positive and negative user experience, to compare and systematize them, and to indicate the business implications of topic modeling findings. For this study, authors collected 9081 online reviews of the SalesForce application from Google Play Store. The corpus is divided into three subsets based on the associated star ratings, where four and five stars indicate positive sentiment, three mixed sentiment, and two and one negative. Each subset is analyzed using the Latent Dirichlet Allocation algorithm, hyper-parameters were fine-tuned, and the experimental models were evaluated with coherence measures to determine the model with the optimal number of topics. The results indicate that the driving factors of positive experience are seamless functionality and reliability, design flaws and performance issues shape negative experiences, and mixed experiences arise from inconsistencies in usability and authentication challenges.

Suggested Citation

  • Olivera Grljević & Mirjana Marić & Rade Božić, 2025. "Exploring Mobile Application User Experience Through Topic Modeling," Sustainability, MDPI, vol. 17(3), pages 1-33, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:1109-:d:1579940
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    References listed on IDEAS

    as
    1. Shounak Pal & Baidyanath Biswas & Rohit Gupta & Ajay Kumar & Shivam Gupta, 2023. "Exploring the factors that affect user experience in mobile-health applications : A text-mining and machine-learning approach," Post-Print hal-04325651, HAL.
    2. Vladimir Vargas-Calderón & Andreina Moros Ochoa & Gilmer Yovani Castro Nieto & Jorge E. Camargo, 2021. "Machine learning for assessing quality of service in the hospitality sector based on customer reviews," Information Technology & Tourism, Springer, vol. 23(3), pages 351-379, September.
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    4. Ziye Shang & Jian Ming Luo & Anthony Kong, 2022. "Topic Modelling for Ski Resorts: An Analysis of Experience Attributes and Seasonality," Sustainability, MDPI, vol. 14(6), pages 1-15, March.
    5. Pal, Shounak & Biswas, Baidyanath & Gupta, Rohit & Kumar, Ajay & Gupta, Shivam, 2023. "Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach," Journal of Business Research, Elsevier, vol. 156(C).
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    Full references (including those not matched with items on IDEAS)

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