用机器学习分析泰国央行新闻稿情绪，为政策沟通研究提供新方法
Sentiment Classification of Thai Central Bank Press Releases Using Supervised Learning
- 采用朴素贝叶斯、随机森林和SVM等监督学习模型
- 小样本下仍可实现有效情绪分类，但高精度需大量标注数据
- 适合关注央行政策沟通与自动化文本分析的研究者
央行沟通对塑造经济预期和货币政策效果至关重要。本研究应用监督学习技术对泰国央行新闻稿进行情绪分类，弥补了以往研究主要依赖词典方法的不足。结果表明，监督学习在小规模数据下仍具有效性，可作为自动化分析的起点。然而，要获得更高准确率和更好泛化能力，仍需大量标注数据，这耗时且需专业知识。研究以泰央行英文沟通内容为案例，验证了多种模型如朴素贝叶斯、随机森林和SVM在中央银行情绪分析中的适用性。
原文摘要 · Abstract (English)
Central bank communication plays a critical role in shaping economic expectations and monetary policy effectiveness. This study applies supervised machine learning techniques to classify the sentiment of press releases from the Bank of Thailand, addressing gaps in research that primarily focus on lexicon-based approaches. My findings show that supervised learning can be an effective method, even with smaller datasets, and serves as a starting point for further automation. However, achieving higher accuracy and better generalization requires a substantial amount of labeled data, which is time-consuming and demands expertise. Using models such as Naïve Bayes, Random Forest and SVM, this study demonstrates the applicability of machine learning for central bank sentiment analysis, with English-language communications from the Thai Central Bank as a case study.
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