A Systematic Review and Bibliometric Study of Climate Change Sentiment Analysis: Trends and Approaches - Karisma Vinda Nissa Kusumawati; Indra Budi; Amanah Ramadiah; Aris Budi Santoso; Prabu Kresna Putra (Scientific Journal of Informatics, 2025)
Purpose: This study aims to map research trends in sentiment analysis on the climate change topic from the beginning of 2020 to the middle of 2025 by utilizing a Systematic Literature Review (SLR) method, along with bibliometric analysis. Climate change represents a worldwide challenge that profoundly affects both the environment and human social interactions, making it essential to comprehend public perceptions of this issue thoroughly. The escalating use of social media is driving an increase in research related to sentiment analysis, which is utilized to gain insights into public opinions and emotions. Methods: Data was collected from six leading databases such as Scopus, ScienceDirect, Taylor and Francis, IEEE Xplore, Sage Journals, and ProQuest, resulting in 3,326 articles. After a screening process using the PRISMA 2020 framework, 42 articles were selected for further analysis.   Result: The findings suggest that Twitter is the predominant platform for climate change sentiment analysis, referenced in 32 articles, while Sina Weibo is mentioned in nine articles, Reddit in two articles, and both Facebook and YouTube in one article each. Of the four approaches assessed, the leading approaches identified in this research are Machine Learning and Deep Learning. In the Machine Learning category, Naïve Bayes is the predominant approach, appearing in 18 articles, followed by Naïve Bayes, cited in 17 articles. Furthermore, Logistic Regression and Random Forest are each mentioned in 13 articles. In the field of Deep Learning methodologies, 10 articles used Convolutional Neural Networks (CNNs), nine articles featured Bi-LSTMs, six articles featured LSTMs, and 13 articles referenced Transformer-based models, particularly BERT. Furthermore, model validation primarily used cross-validation techniques, and the most referenced evaluation metrics were accuracy, recall, and F1-score in 33 articles and precision in 32 articles.   Novelty: The novelty of this research lies in the time of information collection for research on climate change sentiment analysis, spanning 2020 to the middle of 2025. The latest research on a related issue was conducted from 2008 to 2022. Furthermore, this study provides insights into research trends and includes the distribution of articles by country, separating them into Single-Country Publications (SCPs) and Multi-Country Publications (MCPs). This research also presents information on social media platforms, classification approaches, and commonly employed validation and evaluation tools, which differentiate it from prior studies. This analysis is conducted on six leading databases, producing valuable findings for researchers and policymakers.