# User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums

arXiv: https://arxiv.org/abs/2509.11777 · HTML: https://mlsanity.com/paper/2509.11777/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [On the Automated Processing of User Feedback](https://mlsanity.com/paper/2407.15519/) (2024)
- [User Story Tutor (UST) to Support Agile Software Developers](https://mlsanity.com/paper/2406.16259/) (2024)

## Abstract (arXiv)

Customer feedback in industrial forums offers rich but underexplored insights into real-world product experience. Yet systematic analysis remains challenging due to unstructured, domain-specific content and the scarcity of high-quality labeled datasets. This paper presents the User eXperience Perception Insights Dataset (UXPID), a collection of 7130 synthesized and anonymized user feedback branches extracted from a public industrial automation forum. Each JSON record contains multi-post comments enriched with metadata and annotated by a large language model (LLM) for UX insights, user expectations, severity ratings, sentiment, and topic classifications. UXPID is designed to facilitate research in user requirements, user experience (UX) analysis, and AI-driven feedback processing, particularly where privacy and licensing restrictions limit access to real-world data. It supports the training and evaluation of transformer-based models for tasks such as issue detection, sentiment analysis, and requirements extraction in technical forums, providing a valuable resource for advancing NLP methods within industrial product support and software engineering domains.