Traditional text classification studied in the information retrieval and machine learning literature is mainly based on topics. That is, each class or category represents a particular topic, e.g., sports, politics or sciences. However, many real-world problems require more refined classification based on some semantic perspe ctives. For example, in a set of documents about a disease, some documents may report outbreaks of the disease, some may describe how to cure the disease, some may discuss how to prevent the disease, etc. To class ify text at this semantic level, the traditional bag-of-words model is no longer sufficient. In this paper, we study semantic text classification of disease reporting. We show that both keywords and sentence semantic features are very useful for the classification. Our experimental results demonstrated that this integrated approach is highly effective.