A Systematic Review and Bibliometric Study of Climate Change Sentiment Analysis: Trends and Approaches

Abstract

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. This study aims to map research trends in the last five years (2020–2025) by utilizing a Systematic Literature Review (SLR) method along with bibliometric analysis. Data were 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. The research results indicate that Twitter is the most widely used platform for climate change sentiment analysis, followed by Sina Weibo, Reddit, Facebook, and YouTube. Out of the four approaches assessed, the leading approaches highlighted in this research are Machine Learning and Deep Learning. Furthermore, model validation primarily utilizes cross-validation techniques, and the evaluation metrics commonly referenced include accuracy, precision, recall, and F1-score. These discoveries provide valuable resources for researchers and policymakers to develop more targeted environmental communication and policy strategies.