Allergies are one of the most common chronic diseases worldwide. One in five Americans suffer from either allergy or asthma symptoms. With the prevalence of social media, people sharing experiences and opinions on personal health symptoms and concerns on social media are increasing. Mining those publicly available health related data potentially provides valuable healthcare insights. In this paper, we propose a real-time allergy surveillance system that first classifies tweets to identify those that mention actual allergy incidents using bag-of-words model and NaiveBayesMultinomial classifier and applies in-depth text and spatiotemporal analysis. Our experimental results show that the proposed system can detect predominant allergy types with high precision and that allergy-related tweet volume is highly correlated to the weather data (daily maximum temperature). We believe that this is the first study that examines a large-scale social media stream for in-depth analysis of allergy activities.