Bug-related user reviews of mobile applications are highly regarded by developers for their negative influence on apps. Before bug fixing, developers need to manually reproduce the bugs reported in user reviews, which is an extremely time-consuming and tedious task. Hence, it is highly expected to relieve developers from it with some automated approaches. However, it is challenging to achieve the goal since user reviews are hard to understand and poorly informative for bug reproduction (especially lack of reproduction steps). In this paper, we propose \ToolName{} to automatically \textbf{Rep}roduce Android application bugs from user \textbf{Rev}iews. Specifically, \ToolName{} leverages natural language processing techniques to extract valuable information for bug reproduction. % A semantic similarity algorithm is designed to bridge the lexical gap between natural language and the application. % And, a one-step exploration technique is designed to search the actions of bug reproduction, which are missed in user reviews. Then, it ranks GUI components by semantic similarity with the user review and dynamically searches on apps with a novel one-step exploration technique. In the experiments, we generate a benchmark including 63 crash-related user reviews from Google Play, which have been reproduced successfully by three graduate students. On this benchmark, \ToolName{} presents comparable performance with humans, which successfully reproduces 44 user reviews in our benchmark (about 70%) with 432.2 seconds average time. We make the implementation of our approach publicly available, along with the artifacts and experimental data we used~\cite{RepRev}. \end{abstract}