一句话：提出 INFAR 工具，跨时间段与分析维度自动抽取应用评论中的显著/异常主题、主题相关性与因果因素，并以自然语言和交互式图表呈现。
应用评论是用户反馈的重要渠道，其中蕴含对移动应用维护与更新有价值的关键信息。然而评论噪音大、数量多，且随新用户注册和新版本发布持续动态产生，既有自动分类或聚类方法通常只作用于静态评论集合，开发者难以通过跨时间段、跨分析维度（如评论数量与用户评分）的对比获得所需洞见，例如「用户最常遇到的问题是什么」「某类问题是否出现异常增长」。为此，本文提出 INFAR，一个自动从应用评论中抽取洞见（INsights From App Reviews）的工具，抽取的洞见涵盖四类视角：(1) 显著主题，即评分显著偏低的问题主题；(2) 异常主题，即某时间段内评论量骤增的问题主题；(3) 两个主题之间的相关性；(4) 导致评分或评论量变化的因果因素；结果以自然语言描述并结合交互式图表呈现。作者在 6 个热门应用与 12 名工业界从业者上开展实证评估，92%（11/12）的参与者认可 INFAR 所总结洞见的实际有用性。
App reviews play an essential role for users to convey their feedback about using the app. The critical information contained in app reviews can assist app developers for maintaining and updating mobile apps. However, the noisy nature and large-quantity of daily generated app reviews make it difficult to understand essential information carried in app reviews. Several prior studies have proposed methods that can automatically classify or cluster user reviews into a few app topics (e.g., security). These methods usually act on a static collection of user reviews. However, due to the dynamic nature of user feedback (i.e., reviews keep coming as new users register or new app versions being released) and multiple analysis dimensions (e.g., review quantity and user rating), developers still need to spend substantial effort in extracting contrastive information that can only be teased out by comparing data from multiple time periods or analysis dimensions. This is needed to answer questions such as: what kind of issues users are experiencing most? is there an unexpected rise in a particular kind of issue? etc. To address this need, in this paper, we introduce INFAR, a tool that automatically extracts INsights From App Reviews across time periods and analysis dimensions, and presents them in natural language supported by an interactive chart. The insights INFAR extracts include several perspectives: (1) salient topics (i.e., issue topics with significantly lower ratings), (2) abnormal topics (i.e., issue topics that experience a rapid rise in volume during a time period), (3) correlations between two topics, and (4) causal factors to rating or review quantity changes. To evaluate our tool, we conduct an empirical evaluation by involving six popular apps and 12 industrial practitioners, and 92% (11/12) of them approve the practical usefulness of the insights summarized by INFAR.