A hybrid deep learning model for sentiment analysis of COVID-19 tweets with class balancing.

IF 3.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Md Alamin Talukder, Md Ashraf Uddin, Suman Roy, Partho Ghose, Smita Sarker, Ansam Khraisat, Mohsin Kazi, Md Momtazur Rahman, Musawer Hakimi
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Abstract

The widespread dissemination of misinformation and the diverse public sentiment observed during the COVID-19 pandemic highlight the necessity for accurate sentiment analysis of social media discourse. This study proposes a hybrid deep learning (DL) model that integrates Bidirectional Encoder Representations from Transformers (BERT) for contextual feature extraction with Long Short-Term Memory (LSTM) networks for sequential learning to classify COVID-19-related sentiments. To enhance data quality, advanced text preprocessing techniques, including Unicode normalization, contraction expansion, and emoji conversion, are applied. Additionally, to mitigate class imbalance, Random OverSampling (ROS) is employed, leading to significant improvements in model performance. Before applying ROS, the model exhibited lower accuracy and inconsistent performance across sentiment categories. After balancing the dataset, accuracy for binary classification increased to 92.10%, with corresponding precision, sensitivity, and specificity of 92.10%, 92.10%, and 91.50%, respectively. For three-class sentiment classification, accuracy improved to 89.47%, with precision, sensitivity, and specificity of 89.80%, 89.47%, and 94.10%, respectively. In five-class sentiment classification, accuracy reached 81.78%, with precision, sensitivity, and specificity of 82.19%, 81.78%, and 95.28%, respectively. These findings demonstrate the efficacy of combining deep learning-based sentiment analysis with advanced text preprocessing and class balancing techniques for accurately classifying public sentiment related to COVID-19 across multiple sentiment categories.

Abstract Image

Abstract Image

基于类别平衡的COVID-19推文情感分析混合深度学习模型
在2019冠状病毒病大流行期间,错误信息的广泛传播和公众情绪的多样化凸显了对社交媒体话语进行准确情绪分析的必要性。本研究提出了一种混合深度学习(DL)模型,该模型集成了用于上下文特征提取的变形金刚(BERT)双向编码器表示和用于顺序学习的长短期记忆(LSTM)网络,以对covid -19相关情绪进行分类。为了提高数据质量,采用了先进的文本预处理技术,包括Unicode规范化、收缩扩展和表情符号转换。此外,为了缓解类不平衡,采用了随机过采样(ROS),从而显著提高了模型性能。在应用ROS之前,模型在情感类别中表现出较低的准确性和不一致的表现。对数据集进行平衡后,二值分类的准确率提高到92.10%,相应的精密度、灵敏度和特异性分别为92.10%、92.10%和91.50%。对于三级情感分类,准确率提高到89.47%,其中精密度为89.80%,灵敏度为89.47%,特异度为94.10%。在五类情感分类中,准确率达到81.78%,精密度为82.19%,灵敏度为81.78%,特异性为95.28%。这些发现表明,将基于深度学习的情感分析与高级文本预处理和类平衡技术相结合,可以跨多个情感类别准确分类与COVID-19相关的公众情感。
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来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
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