A Hybrid Approach for Sparse Data Classification Based on Topic Model

With an increasing number of short text emerging, sparse text classification is becoming crucial in data mining and information retrieval area. Many efforts have been devoted to improve the efficiency of normal text classification. However, it is still immature in terms of high-dimension and sparse data processing. In this paper, we present a new method which fancifully utilizes Biterm TopicModel (BTM) and Support Vector Machine (SVM). By using BTM, though the dimensionality of training data is reduced significantly, it is still able to keep rich semantic information for the sparse data. We then employ SVM on the generated topics or features. Experiments on 20 Newsgroups and Tencent microblog dataset demonstrate that our approach can achieve excellent classifier performance in terms of precision, recall and F1 measure. Furthermore, it is proved that the proposed method has high efficiency compared with the combination of Latent Dirichlet Allocation (LDA) and SVM. Our method enhances the previous work in this field and establishes the foundation for further studies.