用AI分析文字识别倦怠，可批量监测高压环境中的心理信号
AI-based approach to burnout identification from textual data
- 基于RuBERT模型，结合生成文本与真实评论进行微调
- 能为输入文本生成倦怠概率，支持大规模语料处理
- 适合职场心理健康监测、人力资源管理等场景
本研究提出一种基于人工智能的文本倦怠检测方法，利用自然语言处理技术从文字数据中识别倦怠状态。该方法采用原始用于情感分析的RuBERT模型，并通过两种数据源进行微调：由ChatGPT生成的合成句子以及来自俄语YouTube视频关于倦怠的用户评论。训练后的模型可对输入文本赋予倦怠概率，适用于高压力工作环境中大量书面交流内容的持续监测，以识别潜在的心理健康风险信号。
原文摘要 · Abstract (English)
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.
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