Sentiment analysis on covid-19 vaccine tweets using machine learning and deep learning algorithms

Shazlyn Milleana Shaharudin

QR Code Link :
Type :	article
Subject :	Q Science (General)
ISSN :	2158-107X
Main Author :	Shazlyn Milleana Shaharudin
Title :	Sentiment analysis on covid-19 vaccine tweets using machine learning and deep learning algorithms

Place of Production :	Tanjung Malim
Publisher :	Fakulti Sains dan Matematik
Year of Publication :	2023
Notes :	International Journal of Advanced Computer Science and Applications
Corporate Name :	Universiti Pendidikan Sultan Idris
HTTP Link :	Click to view web link

Abstract : Universiti Pendidikan Sultan Idris

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 Bag-Of-Words and TF-IDF to ensure the best result. Machine learning algorithms like Logistic Regression, Nave 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. 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

References

Khakharia, A.; Shah, V.; Gupta, P. Sentiment Analysis of COVID-19 Vaccine Tweets Using Machine Learning. Rochester, NY June 18, 2021. [Google Scholar]

Liu, B. Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies 2012, 5, 1–167. [Google S]

Bhavya Joshi, Akhilesh Kumar Sharma, Narendra Singh Yadav & Shamik Tiwari (2022) DNN based approach to classify Covid’19 using convolutional neural network and transfer learning, International Journal of Computers and Applications, 44:10, 907-919, DOI:10.1080/1206212X.2021.1983289

Ramani, P., Pradhan, N., Sharma, A.K. (2020). Classification Algorithms to Predict Heart Diseases—A Survey. In: Gupta, M., Konar,

D., Bhattacharyya, S., Biswas, S. (eds) Computer Vision and Machine Intelligence in Medical Image Analysis. Advances in Intelligent Systems and Computing, vol 992. Springer, Singapore.

A. K. Sharma, K. I. Lakhtaria, A. Panwar and S. Vishwakarma, "An Analytical approach based on self organized maps (SOM) in Indian classical music raga clustering," 2014 Seventh International Conference on Contemporary Computing (IC3), Noida, India, 2014, pp. 449-453, doi: 10.1109/IC3.2014.6897215.

Shrivastava, D.K., Sharma, A.K., Bhardwaj, S. (2021). Prediction of COVID’19 Outbreak by Using ML-Based Time-Series Forecasting Approach. In: Singh, P.K., Polkowski, Z., Tanwar, S., Pandey, S.K., Matei, G., Pirvu, D. (eds) Innovations in Information and Communication Technologies (IICT-2020). Advances in Science, Technology & Innovation. Springer, Cham

Alam, K.N.; Khan, M.S.; Dhruba, A.R.; Khan, M.M.; Al-Amri, J.F.; Masud, M.; Rawashdeh, M. Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data. Computational and Mathematical Methods in Medicine 2021, 2021, 1-4. [Publisher Site] [Google Scholar]

Yin, H.; Song, X.; Yang, S.; Li, J. Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions. World Wide Web 2022, 25, 1067–1083. [Google Scholar]

Dubey, A. D. Public Sentiment Analysis of COVID-19 Vaccination Drive in India. Rochester, NY January 24, 2021. [Google Scholar] [CrossRef]

Asghar, Dr. M.; Kundi, F.; Khan, A.; Ahmad, S. Lexicon-Based Sentiment Analysis in the Social Web. Journal of basic and applied scientific research 2014, 4, 238–248. [Google Scholar]

Dua, S. Sentiment Analysis of COVID-19 Vaccine Tweets. Medium. https://towardsdatascience.com/sentiment-analysis-of-covid-19-vaccinetweets-dc6f41a5e1af (accessed 2023-01-17).

Bhagat, K. K.; Mishra, S.; Dixit, A.; Chang, C.-Y. Public Opinions about Online Learning during COVID-19: A Sentiment Analysis Approach. Sustainability 2021, 13, 3346. [Google Scholar] [CrossRef]

Melton, C. A.; Olusanya, O. A.; Ammar, N.; Shaban-Nejad, A. Public Sentiment Analysis and Topic Modeling Regarding COVID-19 Vaccines on the Reddit Social Media Platform: A Call to Action for Strengthening Vaccine Confidence. Journal of Infection and Public Health 2021, 14, 1505–1512. [Google Scholar] [CrossRef]

Villavicencio, C.; Macrohon, J. J.; Inbaraj, X. A.; Jeng, J.-H.; Hsieh, J.- G. Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes. Information 2021, 12, 204. [Google Scholar] [CrossRef]

Sattar, N. S.; Arifuzzaman, S. COVID-19 Vaccination Awareness and Aftermath: Public Sentiment Analysis on Twitter Data and Vaccinated Population Prediction in the USA. Applied Sciences 2021, 11, 6128. [Google Scholar] [CrossRef]

Pristiyono; Ritonga, M.; Ihsan, M. A. A.; Anjar, A.; Rambe, F. H. Sentiment Analysis of COVID-19 Vaccine in Indonesia Using Naïve Bayes Algorithm. IOP Conf. Ser.: Mater. Sci. Eng. 2021, 1088, 012045. [Google Scholar] [CrossRef]

Nurdeni, D. A.; Budi, I.; Santoso, A. B. Sentiment Analysis on Covid19 Vaccines in Indonesia: From The Perspective of Sinovac and Pfizer. In 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT); 2021; pp 122–127. [Google Scholar] [CrossRef]

Ansari, M. T.; Khan, N. Worldwide COVID-19 Vaccines Sentiment Analysis Through Twitter Content. Electronic Journal of General Medicine 2021, 18, em329. [Google Scholar] [CrossRef]

Aygün, İ.; Kaya, B.; Kaya, M. Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic With Deep Learning. IEEE Journal of Biomedical and Health Informatics 2022, 26, 2360–2369. [Google Scholar] [CrossRef]

Alsabban, M. Comparing Two Sentiment Analysis Approaches by Understand the Hesitancy to COVID-19 Vaccine Based on Twitter Data in Two Cultures. In 13th ACM Web Science Conference 2021; WebSci ’21; Association for Computing Machinery: New York, NY, USA, 2021; pp 143–144. [Google Scholar] [CrossRef]

Liu, S.; Liu, J. Public Attitudes toward COVID-19 Vaccines on EnglishLanguage Twitter: A Sentiment Analysis. Vaccine 2021, 39 (39), 5499–5505. [Google Scholar] [CrossRef]

Ikonomakis, M.; Kotsiantis, S.; Tampakas, V. Text Classification Using Machine Learning Techniques. WSEAS TRANSACTIONS on COMPUTERS 2005, 4, 966-974. [Google Scholar]

Tang, D.; Wei, F.; Qin, B.; Liu, T.; Zhou, M. Coooolll: A Deep Learning System for Twitter Sentiment Classification. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014); Association for Computational Linguistics: Dublin, Ireland, 2014; pp 208–212. [Google Scholar]

Zulfiker, Md. S.; Kabir, N.; Biswas, A. A.; Zulfiker, S.; Uddin, M. S. Analyzing the Public Sentiment on COVID-19 Vaccination in Social Media: Bangladesh Context. Array 2022, 15, 100204. [Google Scholar]

Nyawa, S.; Tchuente, D.; Fosso-Wamba, S. COVID-19 Vaccine Hesitancy: A Social Media Analysis Using Deep Learning. Ann Oper Res 2022. [Google Scholar]

Nuser, M.; Alsukhni, E.; Saifan, A.; Khasawneh, R.; Ukkaz, D. Sentiment analysis of covid-19 vaccine with deep learning. Journal of Theoretical and Applied Information Technology 2022, 100, 1-3. [Google Scholar]

Didi, Y.; Walha, A.; Ben Halima, M.; Wali, A. COVID-19 Outbreak Forecasting Based on Vaccine Rates and Tweets Classification. Computational Intelligence and Neuroscience 2022, 2022, e4535541. [Google Scholar]

Soni, K. M.; Gupta, A.; Jain, T. Supervised Machine Learning Approaches for Breast Cancer Classification and a High Performance Recurrent Neural Network. In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA); 2021; pp 1–7. [Google Scholar]

Yacouby, R.; Axman, D. Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models. In Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems; Association for Computational Linguistics: Online, 2020; 79–91. [Google Scholar]

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to previous page