In we media platform such as microblog, emergency has such characteristics as suddenness and having multiple bursting points. Thus, it brings difficulty to emergency detection. Thus, this paper proposed a method of bursty events detection based on sentiment filter. Firstly, the topic was mapped as a hierarchical model according to the method. Then, dynamic adjustment of the model characteristics was made in a timing-driven way so as to detect the new topics of the information. Based on it, the method analyzed the user's emotional attitude toward such topics. The topics were divided into positive and negative emotion tendencies according to the user's emotional attitude. Additionally, the topic full of negative emotion tendency was regarded as emergent topic. The experimental results show that the accuracy and recall of the proposed method are all increased about 10% compared with baseline.

Chinese comparative sentences are more focused in the field of linguistics. Using machine learning methods to identify comparative sentences, however, has only just started. According to the basic principle of the association rules mining algorithm, a method of comparative sentences based on the associated feature vocabulary was proposed. This method regarded word and part of speech as basic elements, defined the connecting way between the table definition core words and interdependent relationship words, and used the Support Vector Machine (SVM) classifier for the identification of comparative sentences. The experimental results show that this method can effectively identify Chinese comparative sentences, and achieves good results in precision, recall and F-measure.