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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| Format: | Thesis |
| Language: | en |
| Published: |
2024
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| Online Access: | https://ir.uitm.edu.my/id/eprint/96310/1/96310.pdf https://ir.uitm.edu.my/id/eprint/96310/ |
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| _version_ | 1840843223398875136 |
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| author | Shahrul Sazali, Amir Danial |
| author_facet | Shahrul Sazali, Amir Danial |
| author_sort | Shahrul Sazali, Amir Danial |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.uitm.ir-96310 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2024 |
| record_format | eprints |
| spelling | my.uitm.ir-963102025-08-18T09:22:41Z https://ir.uitm.edu.my/id/eprint/96310/ Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali 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 NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/96310/1/96310.pdf Shahrul Sazali, Amir Danial (2024) Sentiment analysis on COVID-19 outbreak using PSO-SVM / Amir Danial Shahrul Sazali. (2024) Degree thesis, thesis, Universiti Teknologi MARA, Terengganu. <http://terminalib.uitm.edu.my/96310.pdf> |
| 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 |
| title | 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_short | 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 |
| topic | Expert systems (Computer science). Fuzzy expert systems |
| url | https://ir.uitm.edu.my/id/eprint/96310/1/96310.pdf https://ir.uitm.edu.my/id/eprint/96310/ |
| url_provider | http://ir.uitm.edu.my/ |