Abstract
Bursty topic spreads very quickly and generate huge influence in Weibo. Therefore, bursty topic detection is one of the hot spots of topic detection and tracking. Most of the existing bursty topic detection methods do not consider the basic weight of the bursty term and the filtration of the invalid bursty term. In this paper, we propose a bursty topic detection method BTDF based on calculation of bursty term value and recognition of pseudo bursty term. The proposed BTDF uses topic models and clustering methods to get general topics, and identifies sudden topics from general topics by judging whether topic keywords contain bursty terms. In BTDF, we extract the bursty term by using the basic weight and bursty weight of the term and filter the pseudo bursty terms by analyzing the novelty of the terms. The experiments conducted on Weibo data show that the proposed method achieves better performance in bursty topic detection.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant (No. 61772083, No. 61532006, No. 61877006), and in part by Science and Technology Major Project of Guangxi (GuikeAA18118054).
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Zhang, Q., Du, J., Kou, F., Xue, Z. (2020). Bursty Topic Detection Based on Bursty Term Detection and Filtration. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 594. Springer, Singapore. https://doi.org/10.1007/978-981-32-9698-5_24
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DOI: https://doi.org/10.1007/978-981-32-9698-5_24
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