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首页> 外文会议>IEEE SmartWorld Conference;Ubiquitous Intelligence Computing Conference;Advanced Trusted Computed Conference;Scalable Computing Communications Conference;Cloud Big Data Computing Conference;Internet of People Conference;Smart City Innovation Conference >Mining and Analyzing User Feedback from App Reviews: An Econometric Approach
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Mining and Analyzing User Feedback from App Reviews: An Econometric Approach

机译：挖掘和分析来自应用评论的用户反馈：一种计量经济学方法

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摘要

Mobile application distribution platforms such as Google Play and Apple Store allow users to submit feedback in form of ratings and reviews towards downloaded apps, which actually serve as the communication channel between app users and developers. User reviews of mobile apps often contain complaints or suggestions that are valuable for developers to improve user experience and satisfaction. However, the manual analysis of a large amount of user reviews is a tedious and time consuming task. In this paper, we present CrowdApp, a novel computational framework that reexamines the impact of user reviews on mobile apps (app downloads, etc). Our approach explores multiple app aspects from user reviews, and further analyses the effects of different user feedback towards app downloads using the econometric method. Our econometric analysis reveals that user feedback has impact to app downloads. This work is an exploratory study that integrates econometric methodologies and text mining techniques towards a more complete analysis of the information captured from app reviews, and the results help app developers address the most complained app problems at an early stage.
机译：Google Play和Apple Store等移动应用程序分发平台允许用户以评级的形式提交反馈，并对下载的应用程序进行评论，其实际上作为应用程序用户和开发人员之间的通信渠道。移动应用程序的用户评论通常包含投诉或建议对开发人员来说是有价值的，以提高用户体验和满意度。但是，对大量用户评论的手动分析是乏味且耗时的任务。在本文中，我们呈现CrowdApp，这是一种新的计算框架，重新审视了对移动应用程序（App下载等）的用户评论的影响。我们的方法探讨了来自用户评论的多个应用程序方面，并进一步分析了使用计量计量方法对应用程序下载的不同用户反馈的影响。我们的计量计量分析表明，用户反馈对应用程序下载影响。这项工作是一项探索性研究，将经济学方法和文本挖掘技术集成在应用程序评审中捕获的信息的更完全分析，以及结果帮助App开发人员在早期阶段地解决最令人抱怨的应用问题。

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