Open Access

ARTICLE

Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Mamoona Humayun^1,*, Danish Javed², Nz Jhanjhi², Maram Fahaad Almufareh¹, Saleh Naif Almuayqil¹

1 Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakakah, 72311, Saudi Arabia
2 School of Computer Science, SCS, Taylor’s University, Subang Jaya, 47500, Selangor, Malaysia

* Corresponding Author: Mamoona Humayun. Email:

Computer Systems Science and Engineering 2023, 47(1), 575-591. https://doi.org/10.32604/csse.2023.038765

Received 28 December 2022; Accepted 20 March 2023; Issue published 26 May 2023

Abstract

Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes. People express their unique ideas and views on multiple topics thus providing vast knowledge. Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making. Since the proliferation of COVID-19, it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked. The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed. Hence, this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis. This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment. This can be particularly helpful when dealing with smaller datasets. Furthermore, our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms. This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts. The experimental results validate that our model offers excellent results with a validation accuracy of 92.5% and an F1 measure of 0.92.

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Keywords

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