An Intelligent Machine Learning Framework for Climate Change Sentiment Analysis Using Twitter Data and Support Vector Machine
Authors-P.Navya, M.Satya Pranitha
Keyword-Climate Change, Sentiment Analysis, Twitter Data, Natural Language Processing, Support Vector Machine, Machine Learning, Text Classification, Social Media Analytics, Opinion Min-ing, Environmental Intelligence.
The increasing use of social media platforms has generated vast amounts of public opinion relat-ed to global environmental issues, particularly climate change. Twitter has emerged as one of the most influential platforms where individuals, organizations, and policymakers actively express their views regarding climate-related events and policies. Analyzing these opinions provides valuable insights into public awareness, environmental concerns, and societal attitudes toward climate change. This research presents a machine learning-based sentiment analysis framework that employs Natural Language Processing (NLP) techniques and the Support Vector Machine (SVM) algorithm to classify climate change-related tweets into multiple sentiment categories. A publicly available Twitter dataset obtained from Kaggle is utilized to train and evaluate the pro-posed classification model. Comprehensive preprocessing operations including tokenization, stop-word removal, stemming, and text vectorization are performed to transform unstructured textual data into a machine-readable representation. The trained SVM classifier effectively cap-tures linguistic patterns and semantic relationships present within climate-related discussions. Experimental evaluation demonstrates that the proposed framework achieves reliable sentiment classification performance while maintaining the same implementation methodology and evalua-tion process as the original research. The developed model provides an efficient approach for understanding global public opinion regarding climate change and offers useful information that can support environmental policy development, climate awareness campaigns, and future social media analytics.
Doi-[https://doi.org/10.5281/zenodo.21735215]