Topic Modeling and Sentiment Analysis in NLP

In this chapter, we're going to introduce some well-known modeling methods, and discuss some applications. Topic modeling is a very important part of Natural Language Processing (NLP) and its purpose is to extract semantic pieces of information out of a corpus of documents. We're going to discuss Latent Semantic Analysis (LSA), one of the most famous methods; it's based on the same philosophy already discussed for model-based recommendation systems. We'll also discuss its probabilistic variant, Probabilistic Latent Semantic Analysis (PLSA), which is aimed at building a latent factor probability model without any assumption of prior distributions. On the other hand, the Latent Dirichlet Allocation (LDA) is a similar approach that assumes a prior Dirichlet distribution for latent variables. In the last section, we...