Extracting topics from text documents using Mallet

With an ever-increasing amount of documents in text format nowadays, an important task for any data scientist is to get an overview of a large number of articles with abstracts, summaries, or a list of abstract topics, not because this saves time to read through the articles but to do clustering, classification, semantic relatedness measurement, sentiment analysis, and so on.

In machine learning and natural language processing domain, topic modelling refers to retrieving abstract topics or keywords from text articles using statistical models. In this recipe, we will be using a sophisticated Java-based machine learning and natural language processing library named Mallet, which is an acronym for Machine Learning for Language Toolkit (see http://mallet.cs.umass.edu/). Mallet is widely used in the academia as well as in the industry for the following:

• document classification,

• clustering,

• topic modelling, and

• information extraction.

However, the scope...