Attention Based Encoder Architecture for Automatic Text Classification: A Case Study on Text-news Classification

Abstract

Text classification is a classical problem of natural language processing where given texts are classified into relevant classes. Text classification techniques are extensively used in finding news categories, search engine optimization, automated textual response and many more. With the exponential growth of digital data, it is inevitable that an automatic classification of documents is needed for efficient information retrieval and document archival. Over the years, text classification problems are approached by using classical machine learning techniques like naive bayes(NB), support vector machine(SVM), logistic regression(LR), random forest(RF) and others. In this article, an Attention based deep learning (DL) model is proposed and applied to the news corpus and then the results are compared with the results of classical machine learning models. A comparative analysis of classification accuracy of the DL model with prominent models like LR, RF and NB are presented in this work.