用机器学习帮开发者筛选该回复的用户评论，提升响应效率。
Prioritizing App Reviews for Developer Responses on Google Play
- 基于文本和语义特征构建评分模型，自动判断评论是否需回复。
- XGBoost模型表现最优，F1分数高于其他三种模型。
- 适合资源有限的开发者，快速聚焦高价值用户反馈。
Google Play上应用数量激增，用户评论和评分对应用成功与下载量有显著影响。评论常包含功能建议等重要信息，且用户可随时更新。研究表明，低于三颗星的应用通常被潜在用户回避。自2013年起，开发者可回复评论，有助于解决问题并提升评分与下载量。但回复耗时，仅有13%至18%的开发者实际参与。为此，我们提出一种基于响应优先级的评论优先排序方法。收集并预处理评论数据，提取文本与语义特征，标注评论是否需回复，并训练四种机器学习模型进行评估。使用F1分数、准确率、精确率和召回率等指标衡量性能。结果表明，XGBoost模型在识别需回复评论方面最为有效。
原文摘要 · Abstract (English)
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.
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