Concerning the problem of topics mining and its corresponding public emotion analysis, an analytical method for public emotion evolution was proposed based on Probabilistic Latent Semantic Analysis (PLSA) model. In order to find out the evolutional patterns of the topics, the method started with extracting the subtopics on time series by making use of PLSA model. Then, emotion feature vectors represented by emotion units and their weights which matched with the topic context were established via parsing and ontology lexicon. Next, the strength of public emotion was computed via a fine-grained dimension and the holistic public emotion of the issue. In this case, the method has a deep mining into the evolutional patterns of public emotion which were finally quantified and visualized. The advantage of the method is highlighted by introducing grammatical rules and ontology lexicon in the process of extracting emotion units, which was conducted in a fine-grained dimension to improve the accuracy of extraction. The experimental results show that this method can gain good performance on the evolutional analysis of topics and public emotion on time series and thus proves the positive effect of the method.

For the difficulty of manual annotation on large-scale micro-blog corpus, three automatic annotation methods and an integrated annotation method by voting for Chinese micro-blog corpus were proposed. Three automatic annotation methods included keywords-based annotation method, probability-summation-based annotation method and probability-product-based annotation method. During the process of automatic annotation, firstly, micro-blog corpus were annotated by three annotation methods respectively, and three results were obtained, then the final annotation results were determined by voting method with the integrated strategy. By designing automatic annotation experiment system, experimental results verify the feasibility and effectiveness of the proposed methods, and show that the accuracy of the single annotation method is more than 70%, and it is more than 90% for the voting method.