Scholarlink

<p>Sentiment analysis is critical in understanding public perceptions of evolving currencies such as central bank digital currencies (CBDCs). This study compares three transformer-based models—DistilBERT, RoBERTa, and XLM-RoBERTa—for sentiment classification of UK CBDC-related tweets. Models were fine-tuned on a domain-specific, English-only dataset and evaluated using accuracy, precision, recall, F1-score, training and validation loss across 3 and 5 training epochs. RoBERTa consistently outperformed DistilBERT and XLM-RoBERTa, achieving the highest accuracy and F1-scores. However, extended training epochs in RoBERTa_5 indicated signs of overfitting, making the model fine-tuned for 3 epochs (RoBERTa_3) a suitable model for classifying sentiments on unseen data. These findings provide practical guidance for selecting optimal transformer models for sentiment analysis in specialised financial domains like CBDCs, emphasising the importance of balancing training duration with generalisation capabilities.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Comparative analysis of transformer models for sentiment classification of UK CBDC discourse on X

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月30日

查看全文
  • Guneet Kaur,
  • Saemundur Haraldsson,
  • Andrea Bracciali

摘要

Sentiment analysis is critical in understanding public perceptions of evolving currencies such as central bank digital currencies (CBDCs). This study compares three transformer-based models—DistilBERT, RoBERTa, and XLM-RoBERTa—for sentiment classification of UK CBDC-related tweets. Models were fine-tuned on a domain-specific, English-only dataset and evaluated using accuracy, precision, recall, F1-score, training and validation loss across 3 and 5 training epochs. RoBERTa consistently outperformed DistilBERT and XLM-RoBERTa, achieving the highest accuracy and F1-scores. However, extended training epochs in RoBERTa_5 indicated signs of overfitting, making the model fine-tuned for 3 epochs (RoBERTa_3) a suitable model for classifying sentiments on unseen data. These findings provide practical guidance for selecting optimal transformer models for sentiment analysis in specialised financial domains like CBDCs, emphasising the importance of balancing training duration with generalisation capabilities.