From Noisy News Sentiment Scores to Interpretable Temporal Dynamics: A Bayesian State-Space Model
从含噪新闻情绪分数到可解释的时间动态:贝叶斯状态空间模型
Text-based sentiment indicators are widely used to monitor public and market mood, but weekly sentiment series are noisy by construction. A main reason is that the amount of relevant news changes over time and across categories. As a result, some weekly averages are based on many articles, while others rely on only a few. Existing approaches do not explicitly account for changes in data availability when measuring uncertainty. We present a Bayesian state-space framework that…