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Powered Dirichlet–Hawkes process: challenging textual clustering using a flexible temporal prior

Powered Dirichlet–Hawkes process: challenging textual clustering using a flexible temporal prior 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 retrieves textual clusters, temporal clusters, or a mixture of both with high accuracy. We demonstrate that PDHP generalizes previous work –the Dirichlet–Hawkes process (DHP) and uniform process (UP). Finally, we illustrate the changes induced by PDHP over DHP and UP with a real-world application using Reddit data. We detail how PDHP recovers bursty dynamics and show that its limit case accounts for daily and weekly publication cycles. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Knowledge and Information Systems Springer Journals

Powered Dirichlet–Hawkes process: challenging textual clustering using a flexible temporal prior

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References (26)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0219-1377
eISSN
0219-3116
DOI
10.1007/s10115-022-01731-3
Publisher site
See Article on Publisher Site

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 retrieves textual clusters, temporal clusters, or a mixture of both with high accuracy. We demonstrate that PDHP generalizes previous work –the Dirichlet–Hawkes process (DHP) and uniform process (UP). Finally, we illustrate the changes induced by PDHP over DHP and UP with a real-world application using Reddit data. We detail how PDHP recovers bursty dynamics and show that its limit case accounts for daily and weekly publication cycles.

Journal

Knowledge and Information Systems – Springer Journals

Published: Nov 1, 2022

Keywords: Clustering; Temporal Bayesian prior; Powered Dirichlet process; Hawkes process

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