Le Processus Powered Dirichlet-Hawkes comme A Priori Flexible pour Clustering Temporel de Textes

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 data is weakly informative.

Contribution

The paper presents PDHP, a novel model that generalizes previous approaches and effectively handles cases with weak or uncorrelated textual and temporal information.

Findings

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

PDHP generalizes previous models like DHP and UP.

Application to Reddit data demonstrates practical utility.

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. Furthermore, the textual content of a document is not always correlated to its temporal dynamics. We develop a method to create clusters of textual documents according to both their content and publication time, the Powered Dirichlet-Hawkes process (PDHP). PDHP yields significantly better results than state-of-the-art models when temporal information or textual content is weakly informative. PDHP also alleviates the hypothesis that textual content and temporal dynamics are perfectly correlated. We demonstrate that PDHP generalizes previous work --such as DHP and UP. Finally, we illustrate a possible application using a real-world dataset from Reddit.