Prioritizing App Reviews for Developer Responses on Google Play

TL;DR

This paper presents a machine learning approach to prioritize Google Play app reviews for developer responses, aiming to improve response efficiency and app ratings.

Contribution

It introduces a novel review prioritization method using textual and semantic features with machine learning models, especially XGBoost, to identify reviews needing responses.

Findings

XGBoost achieved the best performance among tested models.

Prioritized reviews can enhance developer response efficiency.

The method helps improve app ratings and user satisfaction.

Abstract

The number of applications in Google Play has increased dramatically in recent years. On Google Play, users can write detailed reviews and rate apps, with these ratings significantly influencing app success and download numbers. Reviews often include notable information like feature requests, which are valuable for software maintenance. Users can update their reviews and ratings anytime. Studies indicate that apps with ratings below three stars are typically avoided by potential users. Since 2013, Google Play has allowed developers to respond to user reviews, helping resolve issues and potentially boosting overall ratings and download rates. However, responding to reviews is time-consuming, and only 13% to 18% of developers engage in this practice. To address this challenge, we propose a method to prioritize reviews based on response priority. We collected and preprocessed review data, extracted both textual and semantic features, and assessed their impact on the importance of responses. We labelled reviews as requiring a response or not and trained four different machine learning models to prioritize them. We evaluated the models performance using metrics such as F1-Score, Accuracy, Precision, and Recall. Our findings indicate that the XGBoost model is the most effective for prioritizing reviews needing a response.