Existing financial sentiment analysis methods often fail to capture the multi-faceted nature of risk in bond markets due to their single-level approach and neglect of temporal dynamics. We propose Multi-Level Sentiment Analysis based on pre-trained language models (PLMs) and large language models (LLMs), a novel framework that systematically integrates firm-specific micro-level sentiment, industry-specific meso-level sentiment, and duration-aware smoothing to model the latency and persistence of textual impact. Applying our framework to the comprehensive Chinese bond market corpus constructed by us (2013–2023, 1.39M texts), we extracted a daily composite sentiment index. Empirical results show statistically measurable improvements in credit spread forecasting when incorporating sentiment (3.25% MAE and 10.96% MAPE reduction), with sentiment shifts closely correlating with major social risk events and firm-specific crises. We open-sourced our model's code on https://anonymous.4open.science/r/fin_senti-E14F.