Abstract
We discuss a temporal text mining task on finding evolutionary patterns of topics from a collection of article revisions. To reveal the evolution of topics, we propose a novel method for finding key phrases that are bursty and significant in terms of revision histories. Then we show a time series clustering method to group phrases that have similar burst histories, where additions and deletions are separately considered, and time series is abstracted by burst detection. In clustering, we use dynamic time warping to measure the distance between time sequences of phrase frequencies. Experimental results show that our method clusters phrases into groups that actually share similar bursts which can be explained by real-world events.
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Liu, Y., Gao, Z., Iwaihara, M. (2017). Identifying Evolutionary Topic Temporal Patterns Based on Bursty Phrase Clustering. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_22
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DOI: https://doi.org/10.1007/978-3-319-63564-4_22
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