The Author-Topic Model for Authors and Documents

TL;DR

The paper introduces the author-topic model, a generative approach extending LDA to incorporate authorship, enabling analysis of author-specific topic distributions and improving understanding of document authorship and content.

Contribution

It presents the author-topic model that integrates authorship into topic modeling, providing a novel way to analyze author-specific topics and document content.

Findings

The model effectively captures author-specific topic distributions.

It outperforms traditional LDA and simple author models in certain tasks.

Applications include author similarity and output entropy analysis.

Abstract

We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include authorship information. Each author is associated with a multinomial distribution over topics and each topic is associated with a multinomial distribution over words. A document with multiple authors is modeled as a distribution over topics that is a mixture of the distributions associated with the authors. We apply the model to a collection of 1,700 NIPS conference papers and 160,000 CiteSeer abstracts. Exact inference is intractable for these datasets and we use Gibbs sampling to estimate the topic and author distributions. We compare the performance with two other generative models for documents, which are special cases of the author-topic model: LDA (a topic model) and a simple author model in which each author is associated with a distribution over words rather than a distribution over topics. We show topics recovered by the author-topic model, and demonstrate applications to computing similarity between authors and entropy of author output.