Affiliations 

  • 1 Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
  • 2 Malayan Banking Berhad, 50050 Kuala Lumpur, Malaysia
  • 3 Datium Insights, 59200 Kuala Lumpur, Malaysia
J Supercomput, 2021 Nov 05.
PMID: 34754140 DOI: 10.1007/s11227-021-04169-6

Abstract

We present a benchmark comparison of several deep learning models including Convolutional Neural Networks, Recurrent Neural Network and Bi-directional Long Short Term Memory, assessed based on various word embedding approaches, including the Bi-directional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. Data augmentation was administered using the Easy Data Augmentation approach resulting in two datasets (original versus augmented). All the models were assessed in two setups, namely 5-class versus 3-class (i.e., compressed version). Findings show the best prediction models were Neural Network-based using Word2Vec, with CNN-RNN-Bi-LSTM producing the highest accuracy (96%) and F-score (91.1%). Individually, RNN was the best model with an accuracy of 87.5% and F-score of 83.5%, while RoBERTa had the best F-score of 73.1%. The study shows that deep learning is better for analyzing the sentiments within the text compared to supervised machine learning and provides a direction for future work and research.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.