# On the Automated Processing of User Feedback

arXiv: https://arxiv.org/abs/2407.15519 · HTML: https://mlsanity.com/paper/2407.15519/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2024.

## Built on by (most cited first, 6 of 6)

- [Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approach](https://mlsanity.com/paper/2408.17404/) (2024)
- [The Impact of Modern AI in Metadata Management](https://mlsanity.com/paper/2501.16605/) (2025)
- [User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums](https://mlsanity.com/paper/2509.11777/) (2025)
- [FeedAIde: Guiding App Users to Submit Rich Feedback Reports by Asking Context-Aware Follow-Up Questions](https://mlsanity.com/paper/2603.04244/) (2026)
- [LikeThis! Empowering App Users to Submit UI Improvement Suggestions Instead of Complaints](https://mlsanity.com/paper/2603.04245/) (2026)
- [LadderTeam: Dual-Agent Laddering Elicitation Framework](https://mlsanity.com/paper/2608.17029/) (2026)

## Abstract (arXiv)

User feedback is becoming an increasingly important source of information for requirements engineering, user interface design, and software engineering in general. Nowadays, user feedback is largely available and easily accessible in social media, product forums, or app stores. Over the last decade, research has shown that user feedback can help software teams: a) better understand how users are actually using specific product features and components, b) faster identify, reproduce, and fix defects, and b) get inspirations for improvements or new features. However, to tap the full potential of feedback, there are two main challenges that need to be solved. First, software vendors must cope with a large quantity of feedback data, which is hard to manage manually. Second, vendors must also cope with a varying quality of feedback as some items might be uninformative, repetitive, or simply wrong. This chapter summarises and pipelines various data mining, machine learning, and natural language processing techniques, including recent Large Language Models, to cope with the quantity and quality challenges. We guide researchers and practitioners through implementing effective, actionable analysis of user feedback for software and requirements engineering.