Text Classification and Categorization through Deep Learning

Abstract

Text classification is one of the important fields in Natural Language Processing (NLP). It assigns text documents into at least two categories in the domain by submitting and deriving a set of features to describe each document and to select the correct category for each one for a set of pre-defined tags or categories based on content. It is even used in several real-life applications such as engineering, science, and marketing and it can be quite effective in addressing problems with labeled data. There are certain Deep Learning (DL) algorithms that can be handy in categorizing text data such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes. This paper illustrates how the text in each document is reviewed and grouped into different sets through the above-mentioned techniques. That way, it will determine which method is best suited for higher accuracy and what possible problems the deep learning model faces using text classification and categorization so that new solutions can be invented to resolve these issues without interfering with the processes in the future.