DeepAI AI Chat
Log In Sign Up

Emerging App Issue Identification via Online Joint Sentiment-Topic Tracing

by   Cuiyun Gao, et al.

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 within acceptable time.


TOUR: Dynamic Topic and Sentiment Analysis of User Reviews for Assisting App Release

App reviews deliver user opinions and emerging issues (e.g., new bugs) a...

AOBTM: Adaptive Online Biterm Topic Modeling for Version Sensitive Short-texts Analysis

Analysis of mobile app reviews has shown its important role in requireme...

Aspect Extraction and Sentiment Classification of Mobile Apps using App-Store Reviews

Understanding of customer sentiment can be useful for product developmen...

Detecting User-Perceived Failure in Mobile Applications via Mining User Traces

Mobile applications (apps) often suffer from failure nowadays. Developer...

On the Identification of the Energy related Issues from the App Reviews

The energy inefficiency of the apps can be a major issue for the app use...

Learning Continuous User Representations through Hybrid Filtering with doc2vec

Players in the online ad ecosystem are struggling to acquire the user da...