A beginner's guide to OCTIS: Optimizing and Comparing Topic Models Is Simple

Topic models are promising generative statistical methods that aim to extract the hidden topics underlying a collection of documents. Typically, topic models have two matrices as output. Then, the top-n words from this matrix with the highest probability are then used to represent a topic. The most popular topic modeling method is Latent Dirichlet Allocation, and many articles are written about its workings and implementations. However, focusing on LDA only is restrictive and might be suboptimal for a given corpus.