ADS Capstone Chronicles Revised

1 Emotionality Analysis of Climate Change Communication in News Media VivianDo

Applied Data Science Master’s Program Shiley Marcos School of Engineering University of San Diego vdo@sandiego.edu

Bryan Flores Applied Data Science Master’s Program Shiley Marcos School of Engineering

University of San Diego bryanflores@sandiego.edu

ABSTRACT

climate change impact, 0.88 for climate crisis, and 0.89 for global warming impact.Our study, using modern methods, aids readers and listeners in identifying the subjects of the media they are consuming, thereby enhancing their understanding of how climate change is portrayed across different platforms. Climate change, also often referred to as the climate crisis, has garnered significant attention in recent decades due to its profound impact on the environment, ecosystems, and human societies worldwide. Scientific consensus overwhelmingly supports that climate change is primarily driven by human activities. The American Association for the Advancement of Science and 17 other scientific associations concluded “the scientific evidence is clear: global climate change caused by human activities is occurring now, and it is a growing threat to society” (American Association for the Advancement of Science, 2009, para. 1). Despite the scientific evidence supporting anthropogenic causes of climate change, media messaging is often clouded with political ideologies and economic interests. The politicization of climate change results in many 1 Introduction 2 Background

Despite mounting evidence, the dissemination of climate change-related information through news channels is frequently mired in political ideologies. This often leads to conflicting messages regarding the validity of climate change and its underlying causes. Given the influential role of the media in shaping public opinion and steering policy discourse, analyzing how climate change is framed within media narratives is crucial to understanding public attitudes and sentiments. Our methodology leveraged the GDELT Project's Climate Change Television transcription dataset, encompassing over 95,000 media snippets. We utilized topic modeling and sentiment analysis to identify key themes and sentiments regarding climate change. Furthermore, we conducted a comparative analysis between traditional Latent Dirichlet Allocation (LDA) and contemporary (ChatGPT GPT-3.5-turbo-0125) topic modeling techniques. We categorized 250 randomly selected snippets into five distinct topic categories: climate change impact, climate crisis, carbon emissions reduction, climate action efforts, and global warming impact.We evaluate GPT model outputs against LDA-derived topic labels using BERTScores, which returns precision, recall, and F-1 scores. The evaluation yielded average F-1 scores as follows: 0.87 for carbon emissions reduction, 0.85 for climate action efforts, 0.84 for

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