Large-scale online systems are complex, fast-evolving, and hardly bug-free despite the testing efforts. Backend system monitoring cannot detect many types of issues, such as UI related bugs, bugs with small impact on backend system indicators, or errors from third-party co-operating systems, etc. However, users are good informers of such issues: They will provide their feedback for any types of issues. This experience paper discusses our design of iFeedback, a tool to perform real-time issue detection based on user feedback texts. Unlike traditional approaches that analyze user feedback with computation-intensive natural language processing algorithms, iFeedback is focusing on fast issue detection, which can serve as a system life-condition monitor. In particular, iFeedback extracts word combination-based indicators from feedback texts. This allows iFeedback to perform fast system anomaly detection with sophisticated machine learning algorithms. iFeedback then further summarizes the texts with an aim to effectively present the anomaly to the developers for root cause analysis. We present our representative experiences in successfully applying iFeedback in tens of large-scale production online service systems in ten months.