Prioritizing App Reviews for Developer Responses on Google Play

2024-10-26

International Distributed Multimedia Systems Conference on Visualization and Visual Languages

AI Summary

To address the challenge of prioritizing developer responses to the vast volume of user reviews on Google Play, this paper proposes an interpretable ranking method that jointly leverages textual and semantic features. Methodologically, it integrates TF-IDF and BERT embeddings to represent review content and employs an XGBoost classifier to identify high-value feedback—such as urgent negative reviews or critical feature requests. A developer-oriented interpretability framework is further designed to transcend the coarse granularity of conventional sentiment analysis. Experimental results demonstrate that the proposed model achieves a significantly higher F1-score than baseline approaches, markedly improving both response accuracy and efficiency. This work represents the first effort to synergistically incorporate semantic modeling and interpretable ranking into app store review governance, providing empirical support for optimizing developers’ time allocation and enhancing user satisfaction.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• App Store Reviews
• Developer Prioritization
• Time Management Optimization

Innovation

• XGBoost Model
• Automatic Prioritization
• App Review Response Optimization