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<p>The widespread adoption of mobile apps has transformed communication, learning, and social behaviors in contemporary society. The reviews of mobile apps play a pivotal role in providing valuable insights into user satisfaction, usability, and concerns, offering developers opportunities to enhance app quality and user experiences. This study focuses on collecting mobile app reviews across diverse app categories for three age groups: children, teens, and adults. Our goal is to focus on two pivotal aspects of mobile app development in different age groups (1) usability, which is of paramount importance to developers, and (2) security/privacy, among top priorities of developers. To achieve these objectives, we employed text classification with a Multinomial Naïve Bayes classifier, coupled with a Bidirectional Encoder Representations from Transformers-Latent Dirichlet Allocation (BERT-LDA) hybrid topic modeling for topic analysis. Further, sentiment analysis was performed to gauge user satisfaction and identify improvement areas. The findings reveal significant variations across mobile app categories and age groups, highlighting distinct usage behaviors. Notably, children’s reviews emphasize usability, while teens and adults prioritize security. This study contributes valuable insights for developers identifying customer needs, addressing usability issues, and understanding user concerns, thereby enhancing overall app quality and user experiences.</p>

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Investigating user reviews of diverse age groups’ mobile apps in terms of usability and security using machine learning

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • Kiranbir Kaur,
  • Madanjit Singh,
  • Munish Saini,
  • Kuljit Kaur Chahal

摘要

The widespread adoption of mobile apps has transformed communication, learning, and social behaviors in contemporary society. The reviews of mobile apps play a pivotal role in providing valuable insights into user satisfaction, usability, and concerns, offering developers opportunities to enhance app quality and user experiences. This study focuses on collecting mobile app reviews across diverse app categories for three age groups: children, teens, and adults. Our goal is to focus on two pivotal aspects of mobile app development in different age groups (1) usability, which is of paramount importance to developers, and (2) security/privacy, among top priorities of developers. To achieve these objectives, we employed text classification with a Multinomial Naïve Bayes classifier, coupled with a Bidirectional Encoder Representations from Transformers-Latent Dirichlet Allocation (BERT-LDA) hybrid topic modeling for topic analysis. Further, sentiment analysis was performed to gauge user satisfaction and identify improvement areas. The findings reveal significant variations across mobile app categories and age groups, highlighting distinct usage behaviors. Notably, children’s reviews emphasize usability, while teens and adults prioritize security. This study contributes valuable insights for developers identifying customer needs, addressing usability issues, and understanding user concerns, thereby enhancing overall app quality and user experiences.