一句话：提出层次多核相关向量机 HMK-RVM，用统一贝叶斯模型同时完成应用评论分类与摘要并利用标签层级关系。
应用商店评论是需求提取的重要来源，但数量大且噪声高。已有自动化方法将评论分类与摘要视为独立任务，流程复杂且开销大，也未利用评论标签间的层次关系。本文提出层次多核相关向量机（HMK-RVM），一种贝叶斯多核方法，在统一模型中集成评论分类与摘要，并借助层次结构学习更准确模型；模型还能揭示学习模式与底层数据，便于解释。作者在两个真实数据集上评估，结果显示性能与当前最优方法相当或更优，同时提供可解释洞见。
App stores enable users to share their experiences directly with the developers in the form of app reviews. Recent studies have shown that the feedback received from users is a valuable source of information for requirements extraction, which encourages app developers to leverage the reviews for app update and maintenance purposes. Follow-up studies proposed automated techniques to help developers filter the large volume of daily and noisy reviews and/or summarize their content. However, all previous studies approached the app reviews classification and summarization as separate tasks, which complicated the process and introduced unnecessary overhead. Moreover, none of those approaches explored the potential of utilizing the hierarchical relationships that exist between the labels of app reviews for the purpose of building a more accurate model. In this work, we propose Hierarchical Multi-Kernel Relevance Vector Machines (HMK-RVM), a Bayesian multi-kernel technique that integrates app review classification and summarization using a unified model. Moreover, it can provide insights into the learned patterns and underlying data for easier model interpretation. We evaluated our proposed approach on two real-world datasets and showed that in addition to the gained insights, the model produces equal or better results than the state of the art.
DOI 原文 ·
@inproceedings{paperbot7053,
title = {Hierarchical Bayesian multi-kernel learning for integrated classification and summarization of app reviews},
author = {Moayad Alshangiti and Weishi Shi and Eduardo Lima and Xumin Liu and Qi Yu},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3549174}
}