研究人员开发了一种方法，通过对投资者微博客进行语义丰富，以在金融NLP中进行更细致的情感分析。该方法使用结构化意见图增强了StockEmotions数据集，提供了超越基本情感和情绪标签的更深层语义理解。通过使用声明式LLM管道和图神经网络（GNN），研究表明，结合这些意见语义可以显著提高各种情绪谱的分类性能。 AI
影响 通过提供对投资者情绪及其背后原因的更深入见解来增强金融NLP。
排序理由 这是一篇发表在arXiv上的研究论文，详细介绍了一种在金融NLP中进行情感分析的新方法。
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arXiv:2605.03092v1 Announce Type: new Abstract: While sentiment analysis is the staple of financial NLP, capturing the nuances of 'why' behind that sentiment remains a challenge. There have been attempts to address this by analysing investor emotions alongside sentiment; however,…
While sentiment analysis is the staple of financial NLP, capturing the nuances of 'why' behind that sentiment remains a challenge. There have been attempts to address this by analysing investor emotions alongside sentiment; however, this does not provide the additional granularit…