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

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 Sup...

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主要作者: Shahrul Sazali, Amir Danial
格式: Thesis
语言:English
出版: 2024
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spelling my-uitm-ir.963102024-06-04T07:20:33Z Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali 2024 Shahrul Sazali, Amir Danial Expert systems (Computer science). Fuzzy expert systems 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. 2024 Thesis https://ir.uitm.edu.my/id/eprint/96310/ https://ir.uitm.edu.my/id/eprint/96310/1/96310.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer and Mathematical Sciences Jantan, Hamidah
institution Universiti Teknologi MARA
collection UiTM Institutional Repository
language English
advisor Jantan, Hamidah
topic Expert systems (Computer science)
Fuzzy expert systems
spellingShingle Expert systems (Computer science)
Fuzzy expert systems
Shahrul Sazali, Amir Danial
Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali
description 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.
format Thesis
qualification_level Bachelor degree
author Shahrul Sazali, Amir Danial
author_facet Shahrul Sazali, Amir Danial
author_sort Shahrul Sazali, Amir Danial
title Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali
title_short Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali
title_full Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali
title_fullStr Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali
title_full_unstemmed Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali
title_sort sentiment analysis on covid-19 outbreak using pso-svm / amir danial shahrul sazali
granting_institution Universiti Teknologi MARA, Terengganu
granting_department Faculty of Computer and Mathematical Sciences
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/96310/1/96310.pdf
_version_ 1804889984755302400