Topic representation model based on microblogging behavior analysis

With the development of microblogging, it has become an important way for people to obtain information, express opinions, and make suggestions. Identifying new topics quickly and accurately from the massive microblogging data plays a crucial role for recommending information and controlling public opinion. The topic representation model provides a basis for topic detection. In this paper, we propose a topic representation model based on user behavior analysis, i.e., microblogging behavior analysis-latent Dirichlet allocation (MBA-LDA) model, for microblogging datasets. Topic-word distribution is acquired by the LDA model which considers information on user behaviors (such as posting, forwarding and commenting) and word distribution among documents within one topic and among different topics. The model also re-assesses the importance of words in topic representation. The basic idea is that the distribution of words within a topic or among different topics has a great influence on the selection of topic expression words. If a word is evenly distributed among all documents of a certain topic, it indicates that the word is the common word of all documents in the topic, and it is more suitable to represent this topic. If a word is more evenly distributed among various topics, it indicates that the word is the common word of all topics, and it can't achieve the purpose of distinguishing topics, so it is less suitable to represent any topic. By experiments with Sina Microblogging's actual data set, the topic model based on the MBA-LDA algorithm makes the representative words more important and increases the differentiation of topic words, which effectively improves the accuracy of subsequent topic detection and evolutionary analysis.