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.

It is difficult for the existing methods to get overall sentiment orientation of the comment text. To solve this problem, the method of multi-document sentiment summarization based on Latent Dirichlet Allocation (LDA) model was proposed. In this method, all the subjective sentences were extracted by sentiment analysis and described by LDA model, then a summary was generated based on the weight of sentences which combined the importance of words and the characteristics of sentences. The experimental results show that this method can effectively identify key sentiment sentences, and achieve good results in precision, recall and F-measure.