LAUSR

Microblog clustering is very important in many web applications. However, microblogs do not provide sufficient word occurrences. Meanwhile the limited length of these messages prevents traditional text clustering approaches from being employed to their full potential. To address this problem, in this paper, we propose a novel semi-supervised learning scheme fully exploring the semantic information to compensate for the limited message length. The key idea is to explore term correlation data, which well captures the semantic information for term weighting and provides greater context for microblogs. We then formulate microblog clustering problem as a semi-supervised non-negative matrix factorization co-clustering framework, which takes advantage of both prior domain knowledge of data points (microblogs) in the form of pair-wise constraints and category knowledge of features (terms). Our approach not only greatly reduces the labor-intensive labeling process, but also deeply exploits hidden information from microblog itself. Extensive experiments are conducted on two real-world microblog datasets. The results demonstrate the effectiveness of the proposed approach which produces promising performance as compared to state-of-the-art methods.