Sentiment Analysis on Climate Change using Twitter Data

The objective is to illuminate the nuanced and complex public opinion surrounding climate change conversations on Twitter. The work employs advanced machine learning methods and natural language processing techniques, notably Support Vector Machines (SVM), to conduct a global-scale sentiment analysis using a sizable dataset that was obtained from a reliable third-party source. This study's main goal is to pinpoint the various ways that emotion is expressed in the context of climate change conversation; both positive and negative expressions are taken into account. The authenticity and utility of the selected third-party dataset—obtained through Kaggle and made possible by a Canadian Innovation Foundation JELF grant awarded to Chris Bausch at the University of Waterloo—are critically evaluated. The SVM-based sentiment analysis demonstrates how well the chosen methodology reflects the complexity of sentiment in climate change debates on Twitter, with an exceptional F1 score of 0.70. The implications of the research include communication strategies for legislators, organizations seeking to reach a global audience, and climate change advocates. By utilizing an external dataset and applying the Support Vector Machines algorithm's sentiment analysis, this study advances our comprehension of the intricate relationship between the public's perspective of climate change and social media conversation. To sum up, this study shows the value of SVM in detecting subtleties of sentiment in huge databases, contributing significant new knowledge to the developing subject at the nexus of environmental consciousness and social media dynamics.