Citation

Jain, Tarun and Verma, Vivek Kumar and Sharma, Akhilesh Kumar and Saini, Bhavna and Purohit, Nishant and -, Bhavika and Mahdin, Hairulnizam and Ahmad, Masitah and Darman, Rozanawati and Haw, Su Cheng 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). ISSN 2158107X

Text Paper_4-Sentiment_Analysis_on_COVID_19_Vaccine_Tweets.pdf - Published Version Restricted to Repository staff only Download (783kB)

Abstract

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, 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.

Item Type:	Article
Uncontrolled Keywords:	Covid-19 vaccine; sentiment analysis; machine learning; deep learning; natural language processing
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 Jul 2023 02:34
Last Modified:	04 Jul 2023 02:34
URII:	http://shdl.mmu.edu.my/id/eprint/11511

Downloads

Edit (login required)