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User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums
Mikhail Kulyabin1, Jan Joosten1, Choro Ulan Uulu2,3
1Siemens AG, Erlangen, Germany.
Scientific Data
|May 20, 2026
Summary
This study introduces the User eXperience Perception Insights Dataset (UXPID), a novel resource for analyzing industrial customer feedback. UXPID aids in developing AI for understanding user experience and requirements from technical forums.
Area of Science:
- Natural Language Processing (NLP)
- Human-Computer Interaction (HCI)
- Software Engineering
Background:
- Industrial forums contain valuable but difficult-to-analyze customer feedback.
- Unstructured, domain-specific data and lack of labeled datasets hinder systematic analysis.
- Privacy and licensing restrict access to real-world industrial user feedback.
Purpose of the Study:
- To present the User eXperience Perception Insights Dataset (UXPID) for UX analysis.
- To facilitate research in user requirements and AI-driven feedback processing.
- To support the development of NLP models for industrial technical forums.
Main Methods:
- Synthesized and anonymized 7130 user feedback branches from an industrial automation forum.
- Annotated feedback with UX insights, expectations, severity, sentiment, and topics using a large language model (LLM).
- Structured data into JSON records with multi-post comments and metadata.
Main Results:
- Created UXPID, a comprehensive dataset for UX research.
- Demonstrated the dataset's utility for training and evaluating transformer-based NLP models.
- Enabled issue detection, sentiment analysis, and requirements extraction from technical feedback.
Conclusions:
- UXPID is a valuable resource for advancing NLP in industrial product support and software engineering.
- The dataset addresses limitations of real-world data scarcity and privacy concerns.
- Facilitates AI-driven analysis of user experience and requirements in technical domains.
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