Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)

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Empirical Analysis of Mobile App Reviews: Machine Learning Approaches to Usability and Security

Authors
Avinash Pal Lidlaan1, 2, *, Srinadh Swamy Majeti1, 2
1School of Engineering, Anurag University, Hyderabad, India
2Venkatapur, Ghatkesar, Medchal Malkajgiri District, Hyderabad, Telangana, 500088, India
*Corresponding author. Email: 24eg306a01@anurag.edu.in
Corresponding Author
Avinash Pal Lidlaan
Available Online 31 December 2025.
DOI
10.2991/978-94-6463-978-0_18How to use a DOI?
Keywords
App review analysis; Machine learning; User-centric threat detection; Mobile finance; Mobile healthcare
Abstract

The steady rise of mobile apps in finance and healthcare has brought more attention to the risks of cybersecurity breaches. While automated systems and algorithmic models are often used to monitor threats, surprisingly little work has examined what users themselves are saying about security in public reviews. This research focused on user feedback from the Google Play Store, where we analyzed over 100,000 reviews across ten finance and ten healthcare apps. We aimed to determine whether users were unintentionally flagging issues related to security or usability. Using a basic term-frequency model to extract keywords, we trained a supervised classifier to separate reviews into those two categories. Interestingly, the finance apps drew more security-related complaints than healthcare apps, which may point to sector-specific trust issues or risks. Overall, the results show that user reviews can offer real signals of technical problems, and they shouldn’t be overlooked when designing safer, more reliable mobile services.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
Series
Advances in Engineering Research
Publication Date
31 December 2025
ISBN
978-94-6463-978-0
ISSN
2352-5401
DOI
10.2991/978-94-6463-978-0_18How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Avinash Pal Lidlaan
AU  - Srinadh Swamy Majeti
PY  - 2025
DA  - 2025/12/31
TI  - Empirical Analysis of Mobile App Reviews: Machine Learning Approaches to Usability and Security
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 199
EP  - 210
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_18
DO  - 10.2991/978-94-6463-978-0_18
ID  - Lidlaan2025
ER  -