<hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:12pt">As a quantitative text analysis method, Latent Dirichlet Allocation (LDA), also often referred to as topic modeling (Blei 2012), has been widely used in Digital Humanities in recent years to explore numerous unstructured text data. When topic modeling is used, one has to deal with many parameters that could influence the result, such as the hyperparameter Alpha and Beta, the topic number, document length, or the number of iterations when updating the model. To understand the impact of these parameters, they must be systematically evaluated. In the last few years, there have been several studies evaluating LDA topic modeling in Digital Humanities or Computational Literary Studies (e.g., Jockers 2013; Schöch 2017; Du 2020; Uglanova & Gius 2020) and the presented paper focuses on evaluating the impact of hyperparameter Alpha on LDA topic models.</hi>