Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali

Shahrul Sazali, Amir Danial (2024) Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali. Degree thesis, Universiti Teknologi MARA, Terengganu.

Abstract

The COVID-19 pandemic has had a huge influence on worldwide society, resulting in widespread lockdowns and considerable changes in everyday life. This project provides the analyzation of attitudes expressed in textual data connected to the COVID-19 outbreak using Particle Swarm Optimization with Support Vector Machines (SVM). This project is driven by the objectives to identify the requirement of Particle Swarm Optimization with Support Vector Machines (PSO-SVM) in sentiment analysis of covid-19 tweets, to apply the PSO-SVM method for sentiment analysis that classified tweets accurately and to evaluate the result of the PSO-SVM model for Covid-19 outbreak sentiment analysis. PSO is an optimization technique by searching decision space by sharing global information between different particles. SVM is a supervised learning model that looks at data for classification by searching hyperplane between classes. The created model achieves 73% accuracy in predicting sentiment of tweets when using a Linear SVM kernel with 70:30 percentage split ratio. The project is set to be improved by using a well-constructed SVM algorithm that can handle large data very well, using a more powerful hardware and unlimiting the language use to train the PSO-SVM.

Metadata

Item Type: Thesis (Degree)
Creators:
Creators
Email / ID Num.
Shahrul Sazali, Amir Danial
2022905711
Contributors:
Contribution
Name
Email / ID Num.
Thesis advisor
Jantan, Hamidah
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Expert systems (Computer science). Fuzzy expert systems
Divisions: Universiti Teknologi MARA, Terengganu > Kuala Terengganu Campus > Faculty of Computer and Mathematical Sciences
Programme: Bachelor of Computer Science (Hons)
Keywords: Particle Swarm Optimization with Support Vector Machines (PSO-SVM), COVID-19 Pandemic
Date: 2024
URI: https://ir.uitm.edu.my/id/eprint/96310
Edit Item
Edit Item

Download

[thumbnail of 96310.pdf] Text
96310.pdf

Download (79kB)

Digital Copy

Digital (fulltext) is available at:

Physical Copy

Physical status and holdings:
Item Status:

ID Number

96310

Indexing

Statistic

Statistic details