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Powered Hawkes-Dirichlet Process: Challenging Textual Clustering using a Flexible Temporal Prior

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Abstract

The textual content of a document and its publication date are intertwined. For example, the publication of a news article on a topic is influenced by previous publications on similar issues, according to underlying temporal dynamics. However, it can be challenging to retrieve meaningful information when textual information conveys little information or when temporal dynamics are hard to unveil. Furthermore, the textual content of a document is not always linked to its temporal dynamics. We develop a flexible method to create clusters of textual documents according to both their content and publication time, the Powered Dirichlet-Hawkes process (PDHP). We show PDHP yields significantly better results than state-of-the-art models when temporal information or textual content is weakly informative. The PDHP also alleviates the hypothesis that textual content and temporal dynamics are always perfectly correlated. PDHP allows retrieving textual clusters, temporal clusters, or a mixture of both with high accuracy when they are not. We demonstrate that PDHP generalizes previous work-such as the Dirichlet-Hawkes process (DHP) and Uniform process (UP). Finally, we illustrate the changes induced by PDHP over DHP and UP in a real-world application using Reddit data.
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hal-03697305 , version 1 (16-06-2022)

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Gael Poux-Medard, Julien Velcin, Sabine Loudcher. Powered Hawkes-Dirichlet Process: Challenging Textual Clustering using a Flexible Temporal Prior. 2021 IEEE International Conference on Data Mining (ICDM), Dec 2021, Auckland, New Zealand. pp.509-518, ⟨10.1109/ICDM51629.2021.00062⟩. ⟨hal-03697305⟩
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