  Topic modeling is an unsupervised method for revealing the hidden semantic
structure of a corpus. It has been increasingly widely adopted as a tool in the
social sciences, including political science, digital humanities and
sociological research in general. One desirable property of topic models is to
allow users to find topics describing a specific aspect of the corpus. A
possible solution is to incorporate domain-specific knowledge into topic
modeling, but this requires a specification from domain experts. We propose a
novel query-driven topic model that allows users to specify a simple query in
words or phrases and return query-related topics, thus avoiding tedious work
from domain experts. Our proposed approach is particularly attractive when the
user-specified query has a low occurrence in a text corpus, making it difficult
for traditional topic models built on word cooccurrence patterns to identify
relevant topics. Experimental results demonstrate the effectiveness of our
model in comparison with both classical topic models and neural topic models.
