NLP Topic Modelling with LDA & Unsupervised Machine Learning
Topic modeling is the process of identifying topics in a set of documents. This can be useful for search engines, customer service automation, semantic analysis, etc. There are multiple methods for doing this, but we will b exploring today's LDA (Latent Dirichlet Allocation). We will see a real-life example of performing LDA on a set of company call reports to see how they are segmented into various topics by unsupervised Machine learning. We can further collect the topics and build a taxonomy for our documents to perform a semantic/ synonym search. We used LDA successfully in our organization to operate on top of our existing elastic document search engine in tandem with language modeling using Word2Vec to obtain absolutely amazing results.