Single stage prediction with embedded topic modeling of online reviews for mobile app management
Single stage prediction with embedded topic modeling of online reviews for mobile app management
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DOI：
10.1214/18-aoas1152
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发表时间：
2016-07
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通讯作者：
Shawn Mankad;Shengli Hu;A. Gopal
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作者：
Shawn Mankad;Shengli Hu;A. Gopal
移动的应用程序是移动的数字经济的基石之一。移动的应用程序与传统企业软件的一个区别特征是在线评论，这是在应用程序市场上提供的，代表了消费者对应用程序的反馈的一个有价值的来源。我们为应用程序开发人员创建了一个有监督的主题建模方法，以使用移动的评论作为质量和客户反馈的有用来源，从而补充了传统的软件测试。该方法是基于一个约束矩阵分解，利用词频和一个给定的响应变量之间的关系，除了条款之间的同现恢复主题，都是预测消费者的情绪和有用的理解底层的文本主题。因子分解与有序回归相结合，以提供关于单个应用性能的在线评论的指导，并随着时间的推移系统地比较不同的应用，以确定功能和消费者情绪的基准。我们应用我们的方法，使用超过100，000个移动的评论的数据集，这些评论来自iTunes和Google Play市场的三个最受欢迎的在线旅行社应用程序。
Mobile apps are one of the building blocks of the mobile digital economy. A differentiating feature of mobile apps to traditional enterprise software is online reviews, which are available on app marketplaces and represent a valuable source of consumer feedback on the app. We create a supervised topic modeling approach for app developers to use mobile reviews as useful sources of quality and customer feedback, thereby complementing traditional software testing. The approach is based on a constrained matrix factorization that leverages the relationship between term frequency and a given response variable in addition to co-occurrences between terms to recover topics that are both predictive of consumer sentiment and useful for understanding the underlying textual themes. The factorization is combined with ordinal regression to provide guidance from online reviews on a single app's performance as well as systematically compare different apps over time for benchmarking of features and consumer sentiment. We apply our approach using a dataset of over 100,000 mobile reviews over several years for three of the most popular online travel agent apps from the iTunes and Google Play marketplaces.