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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.17993 |
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| _version_ | 1866915755244650496 |
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| author | Zavertiaeva, Marina Parshakov, Petr Usanin, Mikhail Smirnov, Aleksei Paklina, Sofia Kibardina, Anastasiia |
| author_facet | Zavertiaeva, Marina Parshakov, Petr Usanin, Mikhail Smirnov, Aleksei Paklina, Sofia Kibardina, Anastasiia |
| contents | This study introduces an AI-based methodology that utilizes natural language processing (NLP) to detect burnout from textual data. The approach relies on a RuBERT model originally trained for sentiment analysis and subsequently fine-tuned for burnout detection using two data sources: synthetic sentences generated with ChatGPT and user comments collected from Russian YouTube videos about burnout. The resulting model assigns a burnout probability to input texts and can be applied to process large volumes of written communication for monitoring burnout-related language signals in high-stress work environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17993 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI-based approach to burnout identification from textual data Zavertiaeva, Marina Parshakov, Petr Usanin, Mikhail Smirnov, Aleksei Paklina, Sofia Kibardina, Anastasiia Computation and Language Artificial Intelligence This study introduces an AI-based methodology that utilizes natural language processing (NLP) to detect burnout from textual data. The approach relies on a RuBERT model originally trained for sentiment analysis and subsequently fine-tuned for burnout detection using two data sources: synthetic sentences generated with ChatGPT and user comments collected from Russian YouTube videos about burnout. The resulting model assigns a burnout probability to input texts and can be applied to process large volumes of written communication for monitoring burnout-related language signals in high-stress work environments. |
| title | AI-based approach to burnout identification from textual data |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2601.17993 |