Natural Language Processing: Python and NLTK

Topic modeling in text

The other famous problem in the context of the text corpus is finding the topics of the given document. The concept of topic modeling can be addressed in many different ways. We typically use LDA (Latent Dirichlet allocation) and LSI (Latent semantic indexing) to apply topic modeling text documents.

Typically, in most of the industries, we have huge volumes of unlabeled text documents. In case of an unlabeled corpus to get the initial insights of the corpus, a topic model is a great option, as it not only gives us topics of relevance, but also categorizes the entire corpus into number of topics given to the algorithm.

We will use a new Python library "gensim" that implements these algorithms for us. So, let's jump to the implementation ...