Topic Modeling with Transformers for Sentence-Level Using Coronavirus Corpus

A Topic Model is a class of generative probabilistic models which has gained widespread use in computer science in recent years, especially in the field of text mining and information retrieval. Since it was first proposed, it has received a large amount of attention and general interest among scientists in many research areas. It allows us to discover the mix of hidden or "latent" subjects that differs from one document to another in a given corpus. But since topic modeling usually requires the prior definition of some parameters - above all the number of topics k to be discovered -, model evaluation is decisive to identify an "optimal" set of parameters for the specific data. Latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers Topic (BerTopic) are the two most popular topic modeling techniques. LDA uses a probabilistic approach whereas BerTopic uses transformers (BERT embeddings) and class-based TF-IDF to create dense clusters © 2022,International Journal of Interactive Mobile Technologies.All Rights Reserved

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. © 2020, World Academy of Research in Science and Engineering. All rights reserved.