  Topic models are statistical methods that extract underlying topics from
document collections. When performing topic modeling, a user usually desires
topics that are coherent, diverse between each other, and that constitute good
document representations for downstream tasks (e.g. document classification).
In this paper, we conduct a multi-objective hyperparameter optimization of
three well-known topic models. The obtained results reveal the conflicting
nature of different objectives and that the training corpus characteristics are
crucial for the hyperparameter selection, suggesting that it is possible to
transfer the optimal hyperparameter configurations between datasets.
