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

2025-09-15

AI Summary

Industrial user feedback in automation forums is highly unstructured and domain-specific, rendering conventional methods ineffective for parsing and quantifying such data—thus hindering its utility in product development. To address this, we introduce UXPID, the first synthetic, domain-specific dataset of industrial user feedback (7,130 instances), encompassing multi-turn discussions, usage contexts, and experiential insights. Leveraging large language models (LLMs), we perform fine-grained annotation across five dimensions: user experience insights, expectations, severity, sentiment scores, and thematic categorization. The dataset is released in JSON format with rich metadata and contextual annotations, enabling training and evaluation of Transformer-based models on tasks including issue detection, sentiment analysis, and requirement extraction. UXPID bridges a critical gap in structured industrial feedback research and establishes a benchmark and open-source resource for AI-driven UX analytics and requirements mining.

Technology Category

• Humans and AI: User Experience and Usability
• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Customer feedback in industrial forums reflect a rich but underexplored source of insight into real-world product experience. These publicly shared discussions offer an organic view of user expectations, frustrations, and success stories shaped by the specific contexts of use. Yet, harnessing this information for systematic analysis remains challenging due to the unstructured and domain-specific nature of the content. The lack of structure and specialized vocabulary makes it difficult for traditional data analysis techniques to accurately interpret, categorize, and quantify the feedback, thereby limiting its potential to inform product development and support strategies. To address these challenges, this paper presents the User eXperience Perception Insights Dataset (UXPID), a collection of 7130 artificially synthesized and anonymized user feedback branches extracted from a public industrial automation forum. Each JavaScript object notation (JSON) record contains multi-post comments related to specific hardware and software products, enriched with metadata and contextual conversation data. Leveraging a large language model (LLM), each branch is systematically analyzed and annotated for UX insights, user expectations, severity and sentiment ratings, and topic classifications. The UXPID dataset 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. UXPID supports the training and evaluation of transformer-based models for tasks such as issue detection, sentiment analysis, and requirements extraction in the context of technical forums.

Problem

• Analyzing unstructured user feedback from industrial forums
• Interpreting domain-specific vocabulary in customer discussions
• Quantifying UX insights for product development strategies

Innovation

• Synthesized user feedback from forums
• LLM-annotated UX insights and metadata
• Transformer model training for technical analysis