User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915990181249024 |
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| author | Kulyabin, Mikhail Joosten, Jan uulu, Choro Ulan Pacheco, Nuno Miguel Martins Ries, Fabian Petridis, Filippos Bosch, Jan Olsson, Helena Holmström |
| author_facet | Kulyabin, Mikhail Joosten, Jan uulu, Choro Ulan Pacheco, Nuno Miguel Martins Ries, Fabian Petridis, Filippos Bosch, Jan Olsson, Helena Holmström |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11777 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums Kulyabin, Mikhail Joosten, Jan uulu, Choro Ulan Pacheco, Nuno Miguel Martins Ries, Fabian Petridis, Filippos Bosch, Jan Olsson, Helena Holmström Computation and Language Machine Learning 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. |
| title | User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2509.11777 |