Root Cause Analysis of Social Media Public Sentiment Toward Carbon Tax Policy Using Support Vector Machine

DOI

10.2991/978-94-6239-828-3_32How to use a DOI?

Keywords

Carbon Tax; Root Cause Analysis; Sentiment Analysis; Support Vector Machine; Topic Modelling; Latent Dirichlet Allocation

Abstract

Indonesia has set a Nationally Determined Contribution (NDC) target to reduce greenhouse gas emissions by 32% or approximately 834 million tons of CO₂e by 2030. To achieve this, the government has implemented a carbon tax policy using a cap-and-tax approach to limit emissions and encourage a transition to clean energy. This study aims to classify public sentiment toward the policy and identify the root causes of negative responses using data from the social media platform X. A total of 721 tweets were collected through web scraping, labeled using a sentiment lexicon, and validated by experts. Sentiment classification was performed using the Support Vector Machine (SVM) algorithm, with the best model using a linear kernel and a 90:10 train-test split, achieving an accuracy of 87%, precision of 80%, recall of 66.67%, and an F1-score of 72%. Further analysis of negative sentiments using Latent Dirichlet Allocation (LDA) revealed four dominant topics such as regulatory uncertainty, economic burden, lack of public trust, and skepticism about environmental impact. The model with four topics was selected based on a coherence score of 33.47% and a diversity score of 92.5%. The study recommends industrial incentives, strengthening the MRV system, and clearer emission caps and roadmaps.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Michael Rahadiyan Wijaya
AU  - Intan Sartika Eris Maghfiroh
AU  - Satrio Hadi Wijoyo
PY  - 2026
DA  - 2026/11/04
TI  - Root Cause Analysis of Social Media Public Sentiment Toward Carbon Tax Policy Using Support Vector Machine
BT  - Proceedings of the 2026 11th International Conference on Sustainable Information Engineering and Technology (SIET 2026)
PB  - Atlantis Press
SP  - 357
EP  - 367
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6239-828-3_32
DO  - 10.2991/978-94-6239-828-3_32
ID  - Wijaya2026
ER  -