  Topic modeling is commonly used to analyze and understand large document
collections. However, in practice, users want to focus on specific aspects or
"targets" rather than the entire corpus. For example, given a large collection
of documents, users may want only a smaller subset which more closely aligns
with their interests, tasks, and domains. In particular, our paper focuses on
large-scale document retrieval with high recall where any missed relevant
documents can be critical. A simple keyword matching search is generally not
effective nor efficient as 1) it is difficult to find a list of keyword queries
that can cover the documents of interest before exploring the dataset, 2) some
documents may not contain the exact keywords of interest but may still be
highly relevant, and 3) some words have multiple meanings, which would result
in irrelevant documents included in the retrieved subset. In this paper, we
present TopicSifter, a visual analytics system for interactive search space
reduction. Our system utilizes targeted topic modeling based on nonnegative
matrix factorization and allows users to give relevance feedback in order to
refine their target and guide the topic modeling to the most relevant results.
