Related papers: Automating Explanation Need Management in App Reviews: A Case Study from the Navigation App Industry

Automating Explanation Need Management in App Reviews: A Case Study from the Navigation App Industry

URL: http://arxiv.org/abs/2501.08087v1
Date: Tue, 14 Jan 2025 12:57:16 GMT
Title: Automating Explanation Need Management in App Reviews: A Case Study from the Navigation App Industry
Authors: Martin Obaidi, Nicolas Voß, Jakob Droste, Hannah Deters, Marc Herrmann, Jannik Fischbach, Kurt Schneider,
Abstract summary: This paper proposes a semi-automated approach to managing explanation needs in user reviews.<n>The approach leverages taxonomy categories to classify reviews and assign them to relevant internal teams or sources for responses.<n>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.
Score: 1.6431822728701062
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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