Indexing and Visualization of Climate Change Narratives Using BERT and Causal Extraction
<|reference_start|>Indexing and Visualization of Climate Change Narratives Using BERT and Causal Extraction: In this study, we propose a methodology to extract, index, and visualize ``climate change narratives'' (stories about the connection between causal and consequential events related to climate change). We use two natural language processing methods, BERT (Bidirectional Encoder Representations from Transformers) and causal extraction, to textually analyze newspaper articles on climate change to extract ``climate change narratives.'' The novelty of the methodology could extract and quantify the causal relationships assumed by the newspaper's writers. Looking at the extracted climate change narratives over time, we find that since 2018, an increasing number of narratives suggest the impact of the development of climate change policy discussion and the implementation of climate change-related policies on corporate behaviors, macroeconomics, and price dynamics. We also observed the recent emergence of narratives focusing on the linkages between climate change-related policies and monetary policy. Furthermore, there is a growing awareness of the negative impacts of natural disasters (e.g., abnormal weather and severe floods) related to climate change on economic activities, and this issue might be perceived as a new challenge for companies and governments. The methodology of this study is expected to be applied to a wide range of fields, as it can analyze causal relationships among various economic topics, including analysis of inflation expectation or monetary policy communication strategy.<|reference_end|>
@article{sakaji2024indexing,
    title={Indexing and Visualization of Climate Change Narratives Using BERT and
  Causal Extraction},
    author={Hiroki Sakaji, Noriyasu Kaneda},
    journal={arXiv preprint arXiv:2408.01745},
    year={2024},
    doi={10.1109/BigData59044.2023.10386320},
    archivePrefix={arXiv},
    eprint={2408.01745},
    primaryClass={cs.CL}
}