A comparative review on deep learning models for text classification

Muhammad Zulqarnain (University Tun Hussain Onn Malaysia (UTHM))
Rozaida Ghazali (Universiti Tun Hussein Onn Malaysia (UTHM))
Yana Mazwin Mohmad Hassim (Universiti Tun Hussein Onn Malaysia (UTHM))
Muhammad Rehan (Universiti Tun Hussein Onn Malaysia (UTHM))

Indonesian Journal of Electrical Engineering and Computer Science
Vol 19, No 1: July 2020

Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handled various classification tasks. DBN have excellent learning capabilities to extracts highly distinguishable features and good for general purpose. CNN have supposed to be better at extracting the position of various related features while RNN is modeling in sequential of long-term dependencies. This paper work shows the systematic comparison of DBN, CNN, and RNN on text classification tasks. Finally, we show the results of deep models by research experiment. The aim of this paper to provides basic guidance about the deep learning models that which models are best for the task of text classification.