Augmenting Text Mining Approaches with Social Network Analysis to Understand the Complex Relationships among Users' Requests: a Case Study of the Android Operating System
Chan Won Lee, Sherlock A. Licorish, Bastin Tony Roy Savarimuthu and, Stephen G. MacDonell

TL;DR
This paper demonstrates how combining text mining with social network analysis enhances understanding of user feedback and relationships in Android app reviews, providing more comprehensive insights for system improvements.
Contribution
The study introduces a novel approach that integrates social network analysis with text mining to improve analysis of user feedback in app reviews.
Findings
Enhanced insights from combined analysis methods
Broader understanding of user relationships and feedback
Potential for improved software system improvements
Abstract
Text mining approaches are being used increasingly for business analytics. In particular, such approaches are now central to understanding users' feedback regarding systems delivered via online application distribution platforms such as Google Play. In such settings, large volumes of reviews of potentially numerous apps and systems means that it is infeasible to use manual mechanisms to extract insights and knowledge that could inform product improvement. In this context of identifying software system improvement options, text mining techniques are used to reveal the features that are mentioned most often as being in need of correction (e.g., GPS), and topics that are associated with features perceived as being defective (e.g., inaccuracy of GPS). Other approaches may supplement such techniques to provide further insights for online communities and solution providers. In this work we…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
