A Machine Learning Approach to Indonesian Climate Change Sentiment
Analysis Using Naive Bayes
Dublin Core
Title
A Machine Learning Approach to Indonesian Climate Change Sentiment
Analysis Using Naive Bayes
Analysis Using Naive Bayes
Subject
Climate Change, Sentiment Analysis, Twitter, Naive Bayes, Indonesia, Public Perception
Description
Climate change poses a significant global challenge, particularly for archipelagic nations such as Indonesia that are highly vulnerable to rising
temperatures and extreme weather events. This study applies machine learning-based sentiment analysis to assess Indonesian public opinion on
climate change using Twitter data. A total of 5,120 Indonesian-language tweets were collected through keyword-based scraping related to climate
and weather conditions. Following text preprocessing (lowercasing, stopword removal, stemming, and cleaning), TF-IDF vectorization was used
to extract the top 400 most significant terms. The dataset was divided into training (80%) and testing (20%) subsets, and a Multinomial Naïve
Bayes classifier was trained to categorize sentiments into positive, neutral, and negative classes. The results show a dominance of negative
sentiment (62%), primarily associated with extreme heat and storm-related events, while neutral (24%) and positive (14%) sentiments were linked
to moderate weather conditions. Model evaluation achieved an F1-score of 0.95 for negative, 0.86 for neutral, and 0.83 for positive sentiment,
yielding a macro-average F1-score of 0.88. The analysis also identified “panas (hot),” “hujan (rain),” and “banjir (flood)” as top lexical indicators
influencing classification. Overall, the findings highlight that Indonesian public sentiment toward climate change is highly reactive to extreme
weather. The study underscores the potential of Naïve Bayes as a baseline model for real-time environmental sentiment monitoring, offering
valuable insights for institutions such as BMKG to enhance public communication and climate awareness strategies.
temperatures and extreme weather events. This study applies machine learning-based sentiment analysis to assess Indonesian public opinion on
climate change using Twitter data. A total of 5,120 Indonesian-language tweets were collected through keyword-based scraping related to climate
and weather conditions. Following text preprocessing (lowercasing, stopword removal, stemming, and cleaning), TF-IDF vectorization was used
to extract the top 400 most significant terms. The dataset was divided into training (80%) and testing (20%) subsets, and a Multinomial Naïve
Bayes classifier was trained to categorize sentiments into positive, neutral, and negative classes. The results show a dominance of negative
sentiment (62%), primarily associated with extreme heat and storm-related events, while neutral (24%) and positive (14%) sentiments were linked
to moderate weather conditions. Model evaluation achieved an F1-score of 0.95 for negative, 0.86 for neutral, and 0.83 for positive sentiment,
yielding a macro-average F1-score of 0.88. The analysis also identified “panas (hot),” “hujan (rain),” and “banjir (flood)” as top lexical indicators
influencing classification. Overall, the findings highlight that Indonesian public sentiment toward climate change is highly reactive to extreme
weather. The study underscores the potential of Naïve Bayes as a baseline model for real-time environmental sentiment monitoring, offering
valuable insights for institutions such as BMKG to enhance public communication and climate awareness strategies.
Creator
Henderi1,*, Sofa Sofiana2
Source
https://ijiis.org/index.php/IJIIS/article/view/246/157
Publisher
Universitas Raharja, Indonesia
Date
january 2025
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Henderi1,*, Sofa Sofiana2, “A Machine Learning Approach to Indonesian Climate Change Sentiment
Analysis Using Naive Bayes,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9725.
Analysis Using Naive Bayes,” Repository Horizon University Indonesia, accessed January 2, 2026, https://repository.horizon.ac.id/items/show/9725.