Augmenting Text Mining Approaches with Social Network Analysis to Understand the Complex Relationships among Users' Requests: a Case Study of the Android Operating System

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 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.