Providing explanations in response to user reviews is a time-consuming and repetitive task for companies, as many reviews present similar issues requiring nearly identical responses. To improve efficiency, this paper proposes a semi-automated approach to managing explanation needs in user reviews. The approach leverages taxonomy categories to classify reviews and assign them to relevant internal teams or sources for responses. 2,366 app reviews from the Google Play Store and Apple App Store were scraped and analyzed using a word and phrase filtering system to detect explanation needs. The detected needs were categorized and assigned to specific internal teams at the company Graphmasters GmbH, using a hierarchical assignment strategy that prioritizes the most relevant teams. Additionally, external sources, such as existing support articles and past review responses, were integrated to provide comprehensive explanations. The system was evaluated through interviews and surveys with the Graphmasters support team, which consists of four employees. The results showed that the hierarchical assignment method improved the accuracy of team assignments, with correct teams being identified in 79.2% of cases. However, challenges in interrater agreement and the need for new responses in certain cases, particularly for Apple App Store reviews, were noted. Future work will focus on refining the taxonomy and enhancing the automation process to reduce manual intervention further.

翻译：针对用户评论提供解释对公司而言是一项耗时且重复的任务，因为许多评论提出了相似的问题，需要近乎相同的回复。为提高效率，本文提出一种半自动化方法来管理用户评论中的解释需求。该方法利用分类学类别对评论进行分类，并将其分配给相关的内部团队或信息来源以生成回复。通过词语和短语过滤系统，从Google Play商店和Apple App商店抓取并分析了2,366条应用评论，以检测解释需求。检测到的需求经过分类后，采用分层分配策略（优先考虑最相关的团队）分配给Graphmasters GmbH公司的特定内部团队。此外，系统整合了外部资源（如现有的支持文章和过往的评论回复）以提供全面的解释。通过对Graphmasters支持团队（由四名员工组成）的访谈和调查对该系统进行了评估。结果表明，分层分配方法提高了团队分配的准确性，在79.2%的情况下能正确识别负责团队。然而，研究也指出了评估者间一致性的挑战，以及在部分情况下（尤其是针对Apple App商店的评论）仍需生成新回复的问题。未来的工作将侧重于完善分类体系并增强自动化流程，以进一步减少人工干预。