  Millions of mobile apps are available in app stores, such as Apple's App
Store and Google Play. For a mobile app, it would be increasingly challenging
to stand out from the enormous competitors and become prevalent among users.
Good user experience and well-designed functionalities are the keys to a
successful app. To achieve this, popular apps usually schedule their updates
frequently. If we can capture the critical app issues faced by users in a
timely and accurate manner, developers can make timely updates, and good user
experience can be ensured. There exist prior studies on analyzing reviews for
detecting emerging app issues. These studies are usually based on topic
modeling or clustering techniques. However, the short-length characteristics
and sentiment of user reviews have not been considered. In this paper, we
propose a novel emerging issue detection approach named MERIT to take into
consideration the two aforementioned characteristics. Specifically, we propose
an Adaptive Online Biterm Sentiment-Topic (AOBST) model for jointly modeling
topics and corresponding sentiments that takes into consideration app versions.
Based on the AOBST model, we infer the topics negatively reflected in user
reviews for one app version, and automatically interpret the meaning of the
topics with most relevant phrases and sentences. Experiments on popular apps
from Google Play and Apple's App Store demonstrate the effectiveness of MERIT
in identifying emerging app issues, improving the state-of-the-art method by
22.3% in terms of F1-score. In terms of efficiency, MERIT can return results
within acceptable time.
