Lead-lag analysis via sparse co-projection in correlated text streams

Authors:
Fangzhao Wu;Yangqiu Song;Shixia Liu;Yongfeng Huang;Zhenyu Liu
Affiliations:
Tsinghua University, Beijing, China;Hong Kong University of Science and Technology, Hong Kong, Hong Kong;Microsoft Research Asia, Beijing, China;Tsinghua University, Beijing, China;China International Communication Center, SCIO, Beijing, China
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

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Abstract

Correlated topical trend detection is very useful in analyzing public and social media influence. In this paper, we propose an algorithm that can both detect the correlation and discover the corresponding keywords that trigger the correlation. To detect the correlation, we use a projection vector to project two text streams onto the same space, and then use a least square cost function to regress one text stream over the other with different time lags. To extract the corresponding keywords, we impose the non-negative sparsity constraints over the projection parameters. In addition, we present an accelerated algorithm based on Nesterov's method to efficiently solve the optimization problem. In our experiments, we use both syntehtic and real data sets to demonstrate the advantages and capabilities of the proposed algorithm over CCA on the follower link prediction problem.