Neurosymbolic AI for mining public opinions about wildfires

• Main Authors: Duong, Cuc, Raghuram, Vethavikashini Chithrra, Lee, Amos, Mao, Rui, Mengaldo, Gianmarco, Cambria, Erik
• Other Authors: Interdisciplinary Graduate School (IGS)
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering::Computer applications::Administrative data processing; Social sciences::Mass media; Neurosymbolic AI; Sentiment Analysis; Emotion Quantification; Opinion Mining; Wildfires
• Online Access: https://hdl.handle.net/10356/170507
• Institution: Nanyang Technological University

Wildfires are among the most threatening hazards to life, property, well-being, and the environment. Studying public opinions about wildfires can help monitor the perception of the impacted communities. Nevertheless, wildfire research is relatively limited compared to other climate-related hazards. This article presents our data mining work on public opinions about wildfires in Australia from 2014 to 2021. Three key aspects are analyzed: the topic of concern, sentiment polarization, and perceived emotions. We propose a data filtering approach to acquire golden samples to train a supervised model for emotion quantification to achieve the last target. The results show that the new model produces a more accurate emotion estimation than the existing lexicon approach. Through data analysis, we find that people have seen wildfires as one of the impacts of climate change; trends of tweets can reflect the damage of wildfires in real life.