<p>Retrospective public opinion analysis provides a scientific basis for subsequent public opinion management and decision optimization. However, current analyses overlook the reflection of popular news and their reading data on the daily concerns of the public, and existing visualization systems are overly complex, hindering their use by domain experts. We designed and implemented a highly usable retrospective public opinion visualization system focusing on massive news data. Based on domain experts’ needs and design principles, the system features intuitive interfaces and interactions. For instance, we provide a temporally stable news map, which integrates contour, time brush, and lens-based interaction to enable rapid hotspot localization, comprehension, and division of evolution phases. To support exploring event evolution in sentiment and popularity, we proposed sentiment metrics to identify events with features like volatility and reversal, plus an event map that clusters events to promote understanding of evolutionary patterns. A comprehensive evaluation systematically demonstrates our system’s effectiveness; a case study on early COVID-19 public opinion in China offers a perspective for analyzing the pandemic via public opinion, facilitating the excavation of pandemic-related narratives.</p> Graphical abstract <p></p>