Topic Modeling Coherence: A Comparative Study between LDA and NMF Models using COVID’19 Corpus

Topic modeling is a method for finding abstract topics in a large collection of documents.With it, it is possible to discover the mixture of hidden or "latent" topics that varies from document to document in a given corpus.As an unsupervised machine learning approach, topic models are not easy to evaluate since there is no labelled "ground truth" data to compare with.However, since topic modeling typically requires defining some parameters beforehand (first and foremost the number of topics k to be discovered), model evaluation is crucial in order to find an "optimal" set of parameters for the given data.Latent Dirichlet allocation (LDA) and Non-Negative Matrix Factorization (NMF) are the two most popular topic modeling techniques.LDA uses a probabilistic approach where as NMF uses matrix factorization approach.In this paper we want to assess which most relevant technique for topic coherence using c_v measure, we have chosen citations's Covid'19 Corpus for experimentations.