User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums
Journal article, 2026

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.

Author

Mikhail Kulyabin

Siemens

Jan Joosten

Siemens

Choro Ulan Uulu

Eindhoven University of Technology

Siemens

Nuno Miguel Martins Pacheco

Siemens

Fabian Ries

Siemens

Filippos Petridis

Siemens

Jan Bosch

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering

Helena Holmstrom Olsson

Malmö university

Scientific data

2052-4463 (eISSN)

Vol. 13 1 762

Subject Categories (SSIF 2025)

Natural Language Processing

Computer Sciences

Computer Systems

DOI

10.1038/s41597-026-07253-9

PubMed

42161978

More information

Latest update

5/29/2026