Public Sentiment on Awareness of Climate Change Based on Support Vector Machine DOI
Norlina Mohd Sabri,

Izzatul Syahirah Ismail,

Nik Marsyahariani Nik Daud

et al.

Pertanika journal of science & technology, Journal Year: 2025, Volume and Issue: 33(S3)

Published: April 23, 2025

Climate change has threatened human society and natural ecosystems, yet public opinion surveys have found that awareness concern are very deficient. If is unaware of climate change, activities such as open burning, deforestation, releasing excessive carbon dioxide gases would not be reduced. There several methods to detect on one the convenient efficient conducting sentiment analysis Twitter. This study uses machine learning techniques collect analyze from Due increasing occurrences disasters worldwide, understanding crucial. The objective based Support Vector Machine (SVM) algorithm. methodology for consists phases: data collection, pre-processing, labeling, feature extraction classifier evaluation. evaluation results indicated SVM achieved a high accuracy 91% with an 80:20 split. model also produced precision, F1-score, recall results. government could use non-governmental organizations (NGOs) help them spread issues. Future work will improve by analyzing non-English tweets using SentiWordNet handle word ambiguity in messages.

Language: Английский

Climate Hoax: The Shift from Scientific Discourse to Speculative Rhetoric in Climate Change Conversations DOI
Samuel Chukwujindu Nwokolo

Next research., Journal Year: 2025, Volume and Issue: unknown, P. 100322 - 100322

Published: April 1, 2025

Language: Английский

Citations

0
Public Sentiment on Awareness of Climate Change Based on Support Vector Machine DOI
Norlina Mohd Sabri,

Izzatul Syahirah Ismail,

Nik Marsyahariani Nik Daud

et al.

Pertanika journal of science & technology, Journal Year: 2025, Volume and Issue: 33(S3)

Published: April 23, 2025

Climate change has threatened human society and natural ecosystems, yet public opinion surveys have found that awareness concern are very deficient. If is unaware of climate change, activities such as open burning, deforestation, releasing excessive carbon dioxide gases would not be reduced. There several methods to detect on one the convenient efficient conducting sentiment analysis Twitter. This study uses machine learning techniques collect analyze from Due increasing occurrences disasters worldwide, understanding crucial. The objective based Support Vector Machine (SVM) algorithm. methodology for consists phases: data collection, pre-processing, labeling, feature extraction classifier evaluation. evaluation results indicated SVM achieved a high accuracy 91% with an 80:20 split. model also produced precision, F1-score, recall results. government could use non-governmental organizations (NGOs) help them spread issues. Future work will improve by analyzing non-English tweets using SentiWordNet handle word ambiguity in messages.

Language: Английский

Citations

0