Emotion recognition, as a core technology in mental health monitoring, has long been constrained by the intrusive nature of data collection methods relying on physiological signals and behavioral cues. Although existing motion-based approaches enable non-intrusive data acquisition, they often overlook the societal dimensions inherent in human behavior. As a result, they often exhibit a significant performance drop in real-world scenarios compared to laboratory settings. In this study, we analyzed the spatial distribution of participants' spatiotemporal trajectories and their visited Points of Interest (POIs), and observed significant differences under varying emotional states. Building on this observation, we propose a novel emotion recognition framework, SE2E, which innovatively incorporates the semantic information of POIs into the emotion recognition task. Specifically, SE2E employs a category-aware semantic embedding mechanism combined with a masked prediction task to ensure that the POI embeddings capture both categorical semantics and contextual information. It then structurally represents individual societal event patterns through a personalized spatiotemporal flow. Finally, a temporal-region consistency attention module is employed to extract continuous representations of societal events, thereby enabling a robust mapping from societal behavior to emotional state. Extensive experimental results demonstrate that SE2E outperforms state-of-the-art methods across multiple benchmarks. To the best of our knowledge, this is the first study to leverage societal event for emotion recognition, offering a new technical direction, benchmark, and insight for future research in the field.