<hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:12pt">The goal of the following tests is to explore the influence of the hyperparameter Alpha. While training topic models, the setting is varied by the value of Alpha</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" style="font-family:Cambria Math">∈ {</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:12pt">0.01, 0.05, 0.1, 0.5, 1, 5, 10, 20, 30, 40, 50, 100} and number of topics</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" style="font-family:Cambria Math">∈ {</hi> <hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:12pt" xml:space="preserve">10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500}. For all other parameter settings, the default values of the topic modeling software were taken. All models were trained without applying hyperparameter optimization, which means that if Alpha is set to 0.1, the Alpha value for each topic is set to 0.1 during the whole training process. Common stop words were removed from both corpora. For technical reasons, namely random initialization in the topic assignment and Gibbs sampling, two topic models from one corpus are not completely identical even if the parameter settings during training are the same. Therefore, ten models were trained for each setting to balance the randomness from the technical side. </hi>