Sentiment analysis on Covid-19 vaccine / Nur Qamarina Hamsa

Hamsa, Nur Qamarina (2021) Sentiment analysis on Covid-19 vaccine / Nur Qamarina Hamsa. Degree thesis, Universiti Teknologi MARA, Perak.


The Covid-19 virus has spread to all countries over the world. In order to handle and survived through this pandemic, a vaccine for Covid-19 was developed to give body a better immune system. However, many people has different point of view towards vaccine. People tend to share their opinions on social media which is Twitter platform such as the effectiveness and side effect of the vaccine. Goverment need to identify their sentiment in order to give better solution and actions related to Covid-19 vaccination. This project performed a sentiment analysis that identify people sentiment on Covid-19 vaccine. The data are collected through Twitter platform by collecting tweets discussing about Covid-19 vaccine and machine learning method was used to develop the sentiment model. The dataset areclean and processing by using natural language toolkit in Pyhton such as stopwords and NeatText library. The model used support vector machine classifier to classify the dataset into its polaritycategories and evaluate the accuracy. Performance metric such as precision, recall and F-score used to validate the model effectively. This project designed a dashboard to visualized overall information of sentiment analysis on Covid-19 vaccine. The dashboard was designed using dash plotly library in python. There are changes of people sentiment around vaccine over the time by monitoring the analysis and statistic provide in the dashboard visualization. This study improves understanding of the public opinion on Covid-19 vaccine.


Item Type: Thesis (Degree)
Email / ID Num.
Hamsa, Nur Qamarina
Email / ID Num.
Thesis advisor
Azizan, Azilawati
Subjects: Q Science > QA Mathematics > Analysis
Divisions: Universiti Teknologi MARA, Perak > Tapah Campus > Faculty of Computer and Mathematical Sciences
Programme: Faculty of Computer Science (Hons.)
Keywords: Sentiment analysis; Covid-19 vaccine
Date: July 2021
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