Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms
—One of the main functions of NLP (Natural Language Processing) is to analyze a sentiment or opinion of the text considered. In this research the objective is to analyze the sentiment in the form of tweets towards the Covid-19 vaccination. In this study, the collected tweets are in the form of a d...
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| Format: | Article |
| Language: | English |
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ijacsa
2023
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| Online Access: | http://eprints.uthm.edu.my/9611/ http://eprints.uthm.edu.my/9611/1/J16133_c49de2f1ee559fa933065163f15ba44f.pdf |
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| author | Tarun Jain, Tarun Jain Vivek Kumar Verma, Vivek Kumar Verma Akhilesh Kumar Sharma, Akhilesh Kumar Sharma Bhavna Saini, Bhavna Saini Nishant Purohit, Nishant Purohit Bhavika, Bhavika Mahdin, Hairulnizam Ahmad, Masitah Darman, Rozanawati Su-Cheng Haw, Su-Cheng Haw Shaharudin, Shazlyn Milleana Arshad, Mohammad Syafwan |
| author_facet | Tarun Jain, Tarun Jain Vivek Kumar Verma, Vivek Kumar Verma Akhilesh Kumar Sharma, Akhilesh Kumar Sharma Bhavna Saini, Bhavna Saini Nishant Purohit, Nishant Purohit Bhavika, Bhavika Mahdin, Hairulnizam Ahmad, Masitah Darman, Rozanawati Su-Cheng Haw, Su-Cheng Haw Shaharudin, Shazlyn Milleana Arshad, Mohammad Syafwan |
| author_sort | Tarun Jain, Tarun Jain |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | —One of the main functions of NLP (Natural Language Processing) is to analyze a sentiment or opinion of the
text considered. In this research the objective is to analyze the sentiment in the form of tweets towards the Covid-19
vaccination. In this study, the collected tweets are in the form of a dataset from Kaggle that have been categorized into positive and negative depending on the polarity of the sentiment in that tweet, to visualize the overall situation. The reviews are translated into vector representations using various techniques, including BagOf-Words and TF-IDF to ensure the best result. Machine learning algorithms like Logistic Regression, Naïve Bayes, Support Vector Machine (SVM) and others, and Deep Learning algorithms like LSTM and Bert were used to train the predictive models. The performance metrics used to test the performance of
the models show that Support Vector Machine (SVM) achieved the highest accuracy of 88.7989% among the machine learning models. Compared to the related research papers the highest accuracy obtained using LSTM is 90.59 % and our model has predicted with the highest accuracy of 90.42% using BERT techniques. |
| first_indexed | 2025-11-15T20:30:44Z |
| format | Article |
| id | uthm-9611 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:30:44Z |
| publishDate | 2023 |
| publisher | ijacsa |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-96112023-08-07T02:27:22Z http://eprints.uthm.edu.my/9611/ Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms Tarun Jain, Tarun Jain Vivek Kumar Verma, Vivek Kumar Verma Akhilesh Kumar Sharma, Akhilesh Kumar Sharma Bhavna Saini, Bhavna Saini Nishant Purohit, Nishant Purohit Bhavika, Bhavika Mahdin, Hairulnizam Ahmad, Masitah Darman, Rozanawati Su-Cheng Haw, Su-Cheng Haw Shaharudin, Shazlyn Milleana Arshad, Mohammad Syafwan T Technology (General) —One of the main functions of NLP (Natural Language Processing) is to analyze a sentiment or opinion of the text considered. In this research the objective is to analyze the sentiment in the form of tweets towards the Covid-19 vaccination. In this study, the collected tweets are in the form of a dataset from Kaggle that have been categorized into positive and negative depending on the polarity of the sentiment in that tweet, to visualize the overall situation. The reviews are translated into vector representations using various techniques, including BagOf-Words and TF-IDF to ensure the best result. Machine learning algorithms like Logistic Regression, Naïve Bayes, Support Vector Machine (SVM) and others, and Deep Learning algorithms like LSTM and Bert were used to train the predictive models. The performance metrics used to test the performance of the models show that Support Vector Machine (SVM) achieved the highest accuracy of 88.7989% among the machine learning models. Compared to the related research papers the highest accuracy obtained using LSTM is 90.59 % and our model has predicted with the highest accuracy of 90.42% using BERT techniques. ijacsa 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9611/1/J16133_c49de2f1ee559fa933065163f15ba44f.pdf Tarun Jain, Tarun Jain and Vivek Kumar Verma, Vivek Kumar Verma and Akhilesh Kumar Sharma, Akhilesh Kumar Sharma and Bhavna Saini, Bhavna Saini and Nishant Purohit, Nishant Purohit and Bhavika, Bhavika and Mahdin, Hairulnizam and Ahmad, Masitah and Darman, Rozanawati and Su-Cheng Haw, Su-Cheng Haw and Shaharudin, Shazlyn Milleana and Arshad, Mohammad Syafwan (2023) Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms. International Journal of Advanced Computer Science and Applications,, 14 (5). pp. 32-41. |
| spellingShingle | T Technology (General) Tarun Jain, Tarun Jain Vivek Kumar Verma, Vivek Kumar Verma Akhilesh Kumar Sharma, Akhilesh Kumar Sharma Bhavna Saini, Bhavna Saini Nishant Purohit, Nishant Purohit Bhavika, Bhavika Mahdin, Hairulnizam Ahmad, Masitah Darman, Rozanawati Su-Cheng Haw, Su-Cheng Haw Shaharudin, Shazlyn Milleana Arshad, Mohammad Syafwan Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms |
| title | Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms |
| title_full | Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms |
| title_fullStr | Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms |
| title_full_unstemmed | Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms |
| title_short | Sentiment Analysis on COVID-19 Vaccine Tweets using Machine Learning and Deep Learning Algorithms |
| title_sort | sentiment analysis on covid-19 vaccine tweets using machine learning and deep learning algorithms |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/9611/ http://eprints.uthm.edu.my/9611/1/J16133_c49de2f1ee559fa933065163f15ba44f.pdf |