Sentiment analysis on Malaysians’ perception about climate change issues based on twitter using support vector machine / Muhammad Abdul Hadi Ahmad Zailani

Climate change presents a global challenge necessitating effective mitigation and adaptation strategies. Public sentiment, a potent driver of climate policies, can be comprehended through Natural Language Processing (NLP) techniques such as sentiment analysis. However, extracting and analyzing data...

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Main Author:	Ahmad Zailani, Muhammad Abdul Hadi
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Integer programming
Online Access:	https://ir.uitm.edu.my/id/eprint/89094/1/89094.pdf
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id	my-uitm-ir.89094
record_format	uketd_dc
spelling	my-uitm-ir.890942024-09-29T03:04:11Z Sentiment analysis on Malaysians’ perception about climate change issues based on twitter using support vector machine / Muhammad Abdul Hadi Ahmad Zailani 2023 Ahmad Zailani, Muhammad Abdul Hadi Integer programming Climate change presents a global challenge necessitating effective mitigation and adaptation strategies. Public sentiment, a potent driver of climate policies, can be comprehended through Natural Language Processing (NLP) techniques such as sentiment analysis. However, extracting and analyzing data from diverse platforms like Twitter poses challenges due to its richness in opinions. This research crafts a web-based system that employs sentiment analysis using Support Vector Machine (SVM) to visualize Malaysian perceptions of climate change. The study adopts a modified waterfall methodology, progressing through phases including requirements gathering, system design, implementation, testing, and documentation. The system acquires and preprocesses Twitter data, employing SVM for sentiment analysis. Attaining an 84.5% classification accuracy, the model effectively gauges public sentiment, with 42.1% negative, 37.4% positive, and the rest neutral sentiments. The prevalence of negative sentiments serves as a strong indication for decision-makers to reassess and improve their approach in addressing climate change, emphasizing the urgency for effective and sustainable mitigation strategies. However, limitations are encountered, including the reliance on data scraping with language parameter adjustments, potentially impacting sentiment analysis accuracy due to linguistic disparities. Moreover, recent Twitter updates imposing reading limits and scraping prevention measures disrupt data acquisition, introducing gaps that may impact sentiment analysis representativeness. Future work recommendations encompass exploring multiple data scraping libraries for language-specific data collection and amalgamating data from diverse social media networks to yield a comprehensive understanding of public sentiment on climate change, thereby enhancing the study's findings and providing broader insights for policy formulation 2023 Thesis https://ir.uitm.edu.my/id/eprint/89094/ https://ir.uitm.edu.my/id/eprint/89094/1/89094.pdf text en public degree Universiti Teknologi MARA, Melaka College of Computing, Informatics and Mathematics
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Integer programming
spellingShingle	Integer programming Ahmad Zailani, Muhammad Abdul Hadi Sentiment analysis on Malaysians’ perception about climate change issues based on twitter using support vector machine / Muhammad Abdul Hadi Ahmad Zailani
description	Climate change presents a global challenge necessitating effective mitigation and adaptation strategies. Public sentiment, a potent driver of climate policies, can be comprehended through Natural Language Processing (NLP) techniques such as sentiment analysis. However, extracting and analyzing data from diverse platforms like Twitter poses challenges due to its richness in opinions. This research crafts a web-based system that employs sentiment analysis using Support Vector Machine (SVM) to visualize Malaysian perceptions of climate change. The study adopts a modified waterfall methodology, progressing through phases including requirements gathering, system design, implementation, testing, and documentation. The system acquires and preprocesses Twitter data, employing SVM for sentiment analysis. Attaining an 84.5% classification accuracy, the model effectively gauges public sentiment, with 42.1% negative, 37.4% positive, and the rest neutral sentiments. The prevalence of negative sentiments serves as a strong indication for decision-makers to reassess and improve their approach in addressing climate change, emphasizing the urgency for effective and sustainable mitigation strategies. However, limitations are encountered, including the reliance on data scraping with language parameter adjustments, potentially impacting sentiment analysis accuracy due to linguistic disparities. Moreover, recent Twitter updates imposing reading limits and scraping prevention measures disrupt data acquisition, introducing gaps that may impact sentiment analysis representativeness. Future work recommendations encompass exploring multiple data scraping libraries for language-specific data collection and amalgamating data from diverse social media networks to yield a comprehensive understanding of public sentiment on climate change, thereby enhancing the study's findings and providing broader insights for policy formulation
format	Thesis
qualification_level	Bachelor degree
author	Ahmad Zailani, Muhammad Abdul Hadi
author_facet	Ahmad Zailani, Muhammad Abdul Hadi
author_sort	Ahmad Zailani, Muhammad Abdul Hadi
title	Sentiment analysis on Malaysians’ perception about climate change issues based on twitter using support vector machine / Muhammad Abdul Hadi Ahmad Zailani
title_short	Sentiment analysis on Malaysians’ perception about climate change issues based on twitter using support vector machine / Muhammad Abdul Hadi Ahmad Zailani
title_full	Sentiment analysis on Malaysians’ perception about climate change issues based on twitter using support vector machine / Muhammad Abdul Hadi Ahmad Zailani
title_fullStr	Sentiment analysis on Malaysians’ perception about climate change issues based on twitter using support vector machine / Muhammad Abdul Hadi Ahmad Zailani
title_full_unstemmed	Sentiment analysis on Malaysians’ perception about climate change issues based on twitter using support vector machine / Muhammad Abdul Hadi Ahmad Zailani
title_sort	sentiment analysis on malaysians’ perception about climate change issues based on twitter using support vector machine / muhammad abdul hadi ahmad zailani
granting_institution	Universiti Teknologi MARA, Melaka
granting_department	College of Computing, Informatics and Mathematics
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/89094/1/89094.pdf
_version_	1811768734518345728

Sentiment analysis on Malaysians’ perception about climate change issues based on twitter using support vector machine / Muhammad Abdul Hadi Ahmad Zailani

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