Abstract
This research explores the effectiveness of four common topic modelling methods for identifying latent themes and topics in unstructured text data: Latent Dirich- let Allocation(LDA), Non-Negative Matrix Factorization(NMF), Top2Vec, and BERTopic. Topic modelling is an essential method for gaining insights from massive amounts of textual data. Top2Vec and BERTopic are recent approaches that use unsupervised neural networks to develop distributed representations of texts and words, whereas NMF and LDA are traditional techniques frequently utilised for topic modelling. This document gives a timeline of important advances in topic modelling, including the development of NMF and LDA, as well as many refinements and additions to LDA. According to the study’s findings, BERTopic surpasses the other approaches, particularly in recognising overlapping and fine- grained subjects. This work emphasises the significance of text processing quality, the variety of subjects in the text, and the right selection of topic modelling methods in efficiently breaking down topics.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
