  App reviews deliver user opinions and emerging issues (e.g., new bugs) about
the app releases. Due to the dynamic nature of app reviews, topics and
sentiment of the reviews would change along with app release versions. Although
several studies have focused on summarizing user opinions by analyzing user
sentiment towards app features, no practical tool is released. The large
quantity of reviews and noise words also necessitates an automated tool for
monitoring user reviews. In this paper, we introduce TOUR for dynamic TOpic and
sentiment analysis of User Reviews. TOUR is able to (i) detect and summarize
emerging app issues over app versions, (ii) identify user sentiment towards app
features, and (iii) prioritize important user reviews for facilitating
developers' examination. The core techniques of TOUR include the online topic
modeling approach and sentiment prediction strategy. TOUR provides entries for
developers to customize the hyper-parameters and the results are presented in
an interactive way. We evaluate TOUR by conducting a developer survey that
involves 15 developers, and all of them confirm the practical usefulness of the
recommended feature changes by TOUR.
