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...
محفوظ في:
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| التنسيق: | أطروحة |
| اللغة: | English |
| منشور في: |
2024
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| الوصول للمادة أونلاين: | https://ir.uitm.edu.my/id/eprint/96310/1/96310.pdf |
| الوسوم: |
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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 |