Abstract
Traditional hot topic detection algorithms cannot show its optimal performance on microblogs for their inherent flaws in constructing short-text representation model, implementing the core algorithm in large corpus with short time and evaluating the algorithms’ qualities during the process of detecting hot topics. In this paper, a novel method for detecting hot topics in microblogs is presented. This approach takes advantage of a probabilistic correlation-based representation measure in order to ensure a dense and low-dimension microblog representation matrix. Besides, we take the clustering as an optimization problem and introduce a discrete particle swarm optimization (DPSO) to simplify the clustering process to detect topics. Furthermore, the clustering quality evaluation criteria is adopted as the optimization objective function for topic detection which can evaluate the algorithms’ qualities after each iteration. Experimental results with corpora containing more than 148,000 twitters show that our algorithm is an effective hot topic detection method for microblog.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (No.61363058), Youth Science and technology support program of Gansu Province (145RJZA232, 145RJYA259), 2016 undergraduate innovation capacity enhancement program and 2016 annual public record open space Fund Project 1505JTCA007.
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Ma, H., Ji, Y., Li, X., Zhou, R. (2016). A Microblog Hot Topic Detection Algorithm Based on Discrete Particle Swarm Optimization. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_23
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DOI: https://doi.org/10.1007/978-3-319-42911-3_23
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