Topic Detection over Online Forum

Topic detection is an hot research in the area of information retrieval. However, the new environment of Internet, the content of which are usually user-generated, asks for new requirements and brings new challenges. Topic detection has to resolve the problem of its lower quality and large amount of noisy. This paper not only provides a solution for detecting hot topics, but also giving its semantic descriptions as result. Our method integrates two kinds of term features (local features and global features), and use single pass clustering to perform topic detection in a web forum. It's efficient to filter non-topic documents and get readable descriptions of topic in our system. By comparison with baseline and topic model LDA, our method gets better performance and readable result.