Keyword Assisted Topic Models

TL;DR

This paper introduces keyATM, a keyword-assisted topic model that improves interpretability and accuracy of topic analysis by incorporating prior keyword labels, addressing limitations of unsupervised models.

Contribution

The paper presents keyATM, a novel supervised topic modeling approach that uses keywords to enhance interpretability and performance over traditional unsupervised models.

Findings

keyATM yields more interpretable topics

It improves document classification accuracy

It is less sensitive to the number of topics

Abstract

In recent years, fully automated content analysis based on probabilistic topic models has become popular among social scientists because of their scalability. The unsupervised nature of the models makes them suitable for exploring topics in a corpus without prior knowledge. However, researchers find that these models often fail to measure specific concepts of substantive interest by inadvertently creating multiple topics with similar content and combining distinct themes into a single topic. In this paper, we empirically demonstrate that providing a small number of keywords can substantially enhance the measurement performance of topic models. An important advantage of the proposed keyword assisted topic model (keyATM) is that the specification of keywords requires researchers to label topics prior to fitting a model to the data. This contrasts with a widespread practice of post-hoc topic interpretation and adjustments that compromises the objectivity of empirical findings. In our application, we find that keyATM provides more interpretable results, has better document classification performance, and is less sensitive to the number of topics than the standard topic models. Finally, we show that keyATM can also incorporate covariates and model time trends. An open-source software package is available for implementing the proposed methodology.