A Comparative Study on Various Text Classification Methods

With the exponential growth in the enhancement of modes of information exchange, the spread of text has become not only substantially faster, but also widespread. Due to this, text has become an indispensable part of all kinds of decisionmaking. Hence, it has become imperative to analyse the methods that can help make sense of this text as efficiently as possible. We shall make an attempt at the same by discussing various tools to make this very task increasingly productive. We shall try to analyse the relationship between the way an algorithm works and how it performs on various sets of data having different types of featurization. We shall analyse featurization techniques such as bag of words/N-grams, Tf-Idf vectorization, average Word2Vec and Tf-Idf Word2Vec.