An Improved Lda Model in Micro-Blog Tags Extracting Based on Multi- Tags

Abstract

This article mainly discusses how to extract the interested information from massive amounts of micro-blogs and recommend right information to user, which is a hot research area in recommendation systems and social networks, too. To solve this problem, a model called Multi-tags Latent Dirichlet Allocation is proposed. Using this model, topics paid attention by users can be mined effectively and the defect of low degree of differentiation for the short blog content is settled. Experiments showed that the tags of user's micro-blog can be figured out with this model which makes users manage their resources at their convenience and others find their needed resources through tags. The results, experimented on real micro-blog data set, indicate that this model works better than traditional model on extracting tags. Standard measuring index Perplexity is applied to this model to estimate the likelihood of new text. If the number of topics is selected appropriately, the accuracy will be raised to almost 10%.

Discovery of Subject of Science and Technology Policy based on LDA Model

“…Facing on the subject discovery problem of large-scale science and technology policies, this paper proposes a science and technology policy set subject modeling method based on LDA model, introduce the concept of subject intensity [6], uses released times and scopes of implementation information of science and technology policies on the basis of computing subject intensity, and realizes the analysis of each subject under different conditions of time and region intensity change trend.…”