<hi xmlns="http://www.tei-c.org/ns/1.0" style="font-size:12pt">Hyperparameter Alpha can refer to two different types of parameters in the context of LDA topic modeling: LDA model parameter and inference algorithm parameter. As a parameter of the LDA model, Alpha determines the properties of a Dirichlet distribution, which is the prior probability distribution of the topic-document distribution. Together, the hyperparameter Alpha and the prior probability distribution determine which topics we expect to occur more frequently in the corpus and how confident we are about them. In practice, when we employ Gibbs Sampling to train our topic model, Alpha is the parameter, which has the smoothing effect on the topic-document distribution and ensures that the probability of each topic in each document is not 0 throughout the entire inference procedure. More importantly, Alpha represents the assumption about the data on how topics are distributed in documents before inferencing the topic model. In other words, the hyperparameter Alpha affects how often each topic occurs in each document. When the alpha value of a topic is set larger in a document, it means that the topic has a greater chance of appearing in that document. And vice versa. For this reason, the setting of Alpha can affect the quality of the inferenced topic model. Therefore, this paper focuses on evaluating the impact of inference algorithm parameter alpha systematically.</hi>