用微调BERT识别文本中的魅力型领导策略，准确率达98.96%
Harnessing Large Language Models: Fine-tuned BERT for Detecting Charismatic Leadership Tactics in Natural Language
- 微调BERT模型识别自然语言中的魅力型领导策略
- 整体检测准确率高达98.96%
- 适合心理学与管理学文本分析研究者
本研究探讨利用微调的双向编码器表示模型（BERT）在自然语言中识别魅力型领导策略（CLTs）。基于自建的大型CLTs语料库，我们训练了一个机器学习模型，可精准识别文本中是否存在此类策略。性能评估显示，所有CLTs的总检测准确率达到98.96%。该研究对心理学与管理学领域具有重要意义，为简化当前复杂的文本魅力评估提供了潜在方法。
原文摘要 · Abstract (English)
This work investigates the identification of Charismatic Leadership Tactics (CLTs) in natural language using a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. Based on an own extensive corpus of CLTs generated and curated for this task, our methodology entails training a machine learning model that is capable of accurately identifying the presence of these tactics in natural language. A performance evaluation is conducted to assess the effectiveness of our model in detecting CLTs. We find that the total accuracy over the detection of all CLTs is 98.96\% The results of this study have significant implications for research in psychology and management, offering potential methods to simplify the currently elaborate assessment of charisma in texts.
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