This study examines user perceptions of the Knimbus and MyLOFT mobile applications, which provide off-campus access to library-subscribed electronic resources. By leveraging sentiment analysis and topic modeling, the research identifies key user concerns and preferences, classifying them into interesting, popular and critical themes.
This study employs a mixed-methods approach using Appbot to analyze 603 user reviews of Knimbus and MyLOFT. Combining sentiment analysis, topic modeling and word cloud visualization, the methodology identifies key user concerns and strengths, offering comparative insights into app performance and guiding targeted improvements.
Results indicate that MyLOFT received more reviews and a higher percentage of positive sentiment than Knimbus, particularly for ease of use and content accessibility. However, MyLOFT users frequently reported app crashes, while Knimbus users struggled with login issues and complex navigation. Both apps faced criticism regarding download limitations and remote access stability. A structured rating system prioritizes critical concerns, highlighting login failures, app crashes and content accessibility as high-priority areas for improvement.
This study is among the first to systematically analyze user perspectives on remote-access library apps using sentiment analysis and topic modeling. It provides a structured framework for assessing user satisfaction and offers actionable insights for app developers and academic institutions to enhance mobile-based electronic resource access.
