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) |
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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 |
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