ArticlePloS one2021
A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- Mapping the "X" Debate: Water Fluoridation Sentiment Analysis With Advanced Machine Learning.Journal of public health dentistry · 2025Article
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- Exploring Topics, Emotions, and Sentiments in Health Organization Posts and Public Responses on Instagram: Content Analysis.JMIR infodemiology · 2025Article
- A Multimodal Pain Sentiment Analysis System Using Ensembled Deep Learning Approaches for IoT-Enabled Healthcare Framework.Sensors (Basel, Switzerland) · 2025Article
- An Intelligent System for Classifying Patient Complaints Using Machine Learning and Natural Language Processing: Development and Validation Study.Journal of medical Internet research · 2025Article
- Understanding user perceptions of DeepSeek: insights from sentiment, topic and network analysis using a Reddit-based study.Frontiers in artificial intelligence · 2025Article
- Differences in Fear and Negativity Levels Between Formal and Informal Health-Related Websites: Analysis of Sentiments and Emotions.Journal of medical Internet research · 2024Article
- Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers.Life (Basel, Switzerland) · 2024Article
- Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers.Heliyon · 2024Article
- The Adverse Effects and Nonmedical Use of Methylphenidate Before and After the Outbreak of COVID-19: Machine Learning Analysis.Journal of medical Internet research · 2023Article
- COVID-19 Vaccine Hesitancy: A Global Public Health and Risk Modelling Framework Using an Environmental Deep Neural Network, Sentiment Classification with Text Mining and Emotional Reactions from COVID-19 Vaccination Tweets.International journal of environmental research and public health · 2023Article
- Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea.Scientific reports · 2023Article
- Covid-19 vaccine hesitancy: Text mining, sentiment analysis and machine learning on COVID-19 vaccination Twitter dataset.Expert systems with applications · 2023Article
- Social media-based COVID-19 sentiment classification model using Bi-LSTM.Expert systems with applications · 2023Article
- Smart sentiment analysis system for pain detection using cutting edge techniques in a smart healthcare framework.Cluster computing · 2023Article
- Predicting bankruptcy of firms using earnings call data and transfer learning.PeerJ. Computer science · 2023Article
- A systematic hybrid machine learning approach for stress prediction.PeerJ. Computer science · 2023Article
- Sentiment Analysis and Comprehensive Evaluation of Supervised Machine Learning Models Using Twitter Data on Russia-Ukraine War.SN computer science · 2023Article
- Improving the Forecasting Accuracy Based on the Lunar Calendar in Modeling Rainfall Levels Using the Bi-LSTM Method through the Grid Search Approach.TheScientificWorldJournal · 2023Article
- COVID-19 Public Opinion: A Twitter Healthcare Data Processing Using Machine Learning Methodologies.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The spread of Covid-19 has resulted in worldwide health concerns. Social media is increasingly used to share news and opinions about it. A realistic assessment of the situation is necessary to utilize resources optimally and appropriately. In this research, we perform Covid-19 tweets sentiment analysis using a supervised machine learning approach. Identification of Covid-19 sentiments from tweets would allow informed decisions for better handling the current pandemic situation. The used dataset is extracted from Twitter using IDs as provided by the IEEE data port. Tweets are extracted by an in-house built crawler that uses the Tweepy library. The dataset is cleaned using the preprocessing techniques and sentiments are extracted using the TextBlob library. The contribution of this work is the performance evaluation of various machine learning classifiers using our proposed feature set. This set is formed by concatenating the bag-of-words and the term frequency-inverse document frequency. Tweets are classified as positive, neutral, or negative. Performance of classifiers is evaluated on the accuracy, precision, recall, and F1 score. For completeness, further investigation is made on the dataset using the Long Short-Term Memory (LSTM) architecture of the deep learning model. The results show that Extra Trees Classifiers outperform all other models by achieving a 0.93 accuracy score using our proposed concatenated features set. The LSTM achieves low accuracy as compared to machine learning classifiers. To demonstrate the effectiveness of our proposed feature set, the results are compared with the Vader sentiment analysis technique based on the GloVe feature extraction approach.
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