Neurosymbolic AI for mining public opinions about wildfires
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....
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sg-ntu-dr.10356-1705072023-09-19T01:57:46Z Neurosymbolic AI for mining public opinions about wildfires Duong, Cuc Raghuram, Vethavikashini Chithrra Lee, Amos Mao, Rui Mengaldo, Gianmarco Cambria, Erik Interdisciplinary Graduate School (IGS) School of Computer Science and Engineering Nanyang Environment and Water Research Institute Engineering::Computer science and engineering::Computer applications::Administrative data processing Social sciences::Mass media Neurosymbolic AI Sentiment Analysis Emotion Quantification Opinion Mining Wildfires 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. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) This research is supported by the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project #A18A2b0046). We also appreciate the support from MOE Tier 2 Grant 22-5191-A0001-0: ‘Prediction-toMitigation with Digital Twins of the Earth’s Weather’. In addition, Cuc Duong is supported by Nanyang Research Scholarship 2023-09-19T01:56:35Z 2023-09-19T01:56:35Z 2023 Journal Article Duong, C., Raghuram, V. C., Lee, A., Mao, R., Mengaldo, G. & Cambria, E. (2023). Neurosymbolic AI for mining public opinions about wildfires. Cognitive Computation. https://dx.doi.org/10.1007/s12559-023-10195-8 1866-9956 https://hdl.handle.net/10356/170507 10.1007/s12559-023-10195-8 en A18A2b0046 MOE-T2-22-5191-A0001-0 Cognitive Computation © 2023 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. All rights reserved. |
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Engineering::Computer science and engineering::Computer applications::Administrative data processing Social sciences::Mass media Neurosymbolic AI Sentiment Analysis Emotion Quantification Opinion Mining Wildfires Duong, Cuc Raghuram, Vethavikashini Chithrra Lee, Amos Mao, Rui Mengaldo, Gianmarco Cambria, Erik Neurosymbolic AI for mining public opinions about wildfires |
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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. |
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Interdisciplinary Graduate School (IGS) |
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Interdisciplinary Graduate School (IGS) Duong, Cuc Raghuram, Vethavikashini Chithrra Lee, Amos Mao, Rui Mengaldo, Gianmarco Cambria, Erik |
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Article |
author |
Duong, Cuc Raghuram, Vethavikashini Chithrra Lee, Amos Mao, Rui Mengaldo, Gianmarco Cambria, Erik |
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Duong, Cuc |
title |
Neurosymbolic AI for mining public opinions about wildfires |
title_short |
Neurosymbolic AI for mining public opinions about wildfires |
title_full |
Neurosymbolic AI for mining public opinions about wildfires |
title_fullStr |
Neurosymbolic AI for mining public opinions about wildfires |
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Neurosymbolic AI for mining public opinions about wildfires |
title_sort |
neurosymbolic ai for mining public opinions about wildfires |
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2023 |
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https://hdl.handle.net/10356/170507 |
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1779156269705199616 |