Filtering out the noise in short text topic modeling

Abstract

Nowadays, massive short texts, such as social media posts and newspaper titles, are available on the Internet. Analyzing these short texts is very significant for many content analysis tasks. However, the commonly used text analysis tools, i.e., topic models, lose effectiveness on short texts because of the sparsity and noise problems. Recent topic models mainly attempt to solve the sparsity problem, but neglect the noise issue. To address this, we propose a common semantics topic model (CSTM) in this paper. The key idea is to introduce a new type of topic, namely common topic, to gather the noise words. The experimental results on real-world datasets indicate that our CSTM outperforms the existing short text topic models on the traditional tasks.