Climate Change Sentiment Analysis Using Domain Specific Bidirectional Encoder Representations From Transformers

Climate change’s impact on human health poses unprecedented and diverse challenges. Unless proactive measures based on solid evidence are implemented, these threats will likely escalate and continue to endanger human well-being. The escalating advancements in information and communication technologies have facilitated the widespread availability and utilization of social media platforms. Individuals utilize platforms such as Twitter and Facebook to express their opinions, thoughts, and critiques on diverse subjects, encompassing the pressing issue of climate change. The proliferation of climate change-related content on social media necessitates comprehensive analysis to glean meaningful insights. This paper employs natural language processing (NLP) techniques to analyze climate change discourse and quantify the sentiment of climate change-related tweets. We collected a total number of 5506 tweets for the period of January 2022 and February 2023 and manually labeled them to make the dataset for this experiment. ClimateBERT, a pre-trained model fine-tuned specifically on the climate change domain was used to generate the context vectors. Several machine learning algorithms with different feature encoding techniques, such as TF-IDF and BERT, have been implemented to classify user sentiments. When comparing the performance of the classifiers using different evaluation metrics such as precision, recall, accuracy, and f-measure, the ClimateBERT + Random Forest model is found to be outperforming all the other baselines with an accuracy of 90.22%, recall of 85.22%, and an f-measure of 85.47%. The findings from this experiment unearth valuable insights into public sentiment and the entities associated with climate change discourse. Policymakers, researchers, and organizations can leverage such analyses to understand public perceptions, identify influential actors, and devise informed strategies to address climate change challenges.