A Novel Content Enriching Model for Microblog Using News Corpus

In this paper, we propose a novel model for enriching the content of microblogs by exploiting external knowledge, thus improving the data sparseness problem in short text classification. We assume that microblogs share the same topics with external knowledge. We first build an optimization model to infer the topics of microblogs by employing the topic-word distribution of the external knowledge. Then the content of microblogs is further enriched by relevant words from external knowledge. Experiments on microblog classification show that our approach is effective and outperforms traditional text classification methods.