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

TL;DR

This paper introduces the Powered Dirichlet-Hawkes process (PDHP), a flexible clustering method that jointly models textual content and publication time, outperforming existing models especially when either source of information is weak or uncorrelated.

Contribution

The paper presents PDHP, a novel model that generalizes previous approaches, effectively capturing complex temporal and textual dynamics in document clustering.

Findings

PDHP outperforms state-of-the-art models in weakly informative scenarios.

PDHP can retrieve pure textual, temporal, or mixed clusters with high accuracy.

Application to Reddit data demonstrates PDHP's practical advantages.

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.