Abstract
Clustering microblogs is very important in many web applications. In this paper, we propose a semi-supervised Nonnegative Matrix Factorization clustering method based on term correlation. The key idea is to explore term correlation data, which well captures the semantic information for term weighting. We then formulate microblog clustering problem as a non-negative matrix factorization using word-level constraints. Empirical study of real-world dataset shows the superior performance of our framework in handling noisy and short microblogs.
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Ma, H., Jia, M., Shi, Y., Hao, Z. (2014). Semi-supervised Nonnegative Matrix Factorization for Microblog Clustering Based on Term Correlation. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_46
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DOI: https://doi.org/10.1007/978-3-319-11116-2_46
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11115-5
Online ISBN: 978-3-319-11116-2
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