Augmenting Text Mining Approaches with Social Network Analysis to Understand the Complex Relationships among Users' Requests: a Case Study of the Android Operating System
This work addresses the challenge for businesses and developers in efficiently analyzing user feedback from platforms like Google Play, though it appears incremental as it supplements existing text mining methods.
The authors tackled the problem of extracting insights from large volumes of user reviews for software systems like Android by augmenting text mining with social network analysis, resulting in a demonstration that combining these techniques provides a broader range of insights for product improvement.
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 augment text mining approaches with social network analysis to demonstrate the utility of using multiple techniques. Our outcomes suggest that text mining approaches may indeed be supplemented with other methods to deliver a broader range of insights.