Expressions in social media can provide a rapid insight into people's reactions to events, such as periods of climatic stress. This study explored the link between climatic stressors and negative sentiment on the X platform in Germany to inform climate-related health policies and interventions. Natural language processing was used to standardize the text, and a comprehensive approach for sentiment analysis was utilized. We then conducted spatiotemporal modeling fitted using integrated nested laplace approximation (INLA). Our findings indicate that higher and lower level of temperature and precipitation is correlated with an increase and decrease in the relative risk of negative sentiments, respectively. The findings of this study illustrate that human sentiment of distress in social media varies with space and time about exposure to climate stressors. This emotional indicator of human exposure and responses to climate stress indicates potential physical and mental health impacts among the affected populations.