# Introduction The transformer-based sentiment models have been increasingly used for analyzing opinions on social media. However, temporal changes can compromise their stability and accuracy. # Methodology The author analyzes the stability of sentiment models through a zero-training approach, applying his skills to authentic social media flows from large events. The method was evaluated on three different transformer architectures and 12,279 authentic social posts. # Results The results show significant model instability with accuracy drops reaching 23.4% during event-driven periods. The author introduced four new drift metrics that surpass embedding-based baselines and are suitable for production deployment. # Conclusion The discovery of these temporal changes can help improve the stability of sentiment models and ensure more precise results. This zero-training method represents a good solution for deploying sentiment models in dynamic situations.