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.

翻译：特别是,这些方法现在对于了解用户对通过谷歌Play等在线应用分配平台提供的系统的反馈至关重要。在这种环境下,大量审查潜在众多的应用程序和系统意味着,不宜使用人工机制来获取有助于产品改进的见解和知识。在确定软件系统改进选项方面,使用文字采矿技术来揭示最经常提到的需要纠正的特征(例如全球定位系统),以及与被认为有缺陷的特征(例如GPS的不准确性)相关的专题。其他方法可以补充这些技术,为在线社区和解决方案提供者提供进一步的见解。在这项工作中,我们用社会网络分析来增加文字采矿方法,以证明使用多种技术的效用。我们的成果表明,文本采矿方法确实可以用其他方法来补充,以提供更广泛的见解。