1 research outputs found
Keyword Assisted Topic Models
For a long time, many social scientists have conducted content analysis by
using their substantive knowledge and manually coding documents. In recent
years, however, fully automated content analysis based on probabilistic topic
models has become increasingly popular because of their scalability.
Unfortunately, applied researchers find that these models often fail to yield
topics of their substantive interest by inadvertently creating multiple topics
with similar content and combining different themes into a single topic. In
this paper, we empirically demonstrate that providing topic models with a small
number of keywords can substantially improve their performance. The proposed
keyword assisted topic model (keyATM) offers an important advantage 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 applications, we find that the 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 the keyATM can also incorporate covariates and model time trends.
An open-source software package is available for implementing the proposed
methodology