@article{oai:miyazaki-u.repo.nii.ac.jp:00006205,
 author = {Nann, Hwan Khun and Thi, Thi Zin and Yokota, Mitsuhiro and Hninn, Aye Thant},
 journal = {宮崎大学工学部紀要, Memoirs of Faculty of Engineering, University of Miyazaki},
 month = {Sep},
 note = {Identifying the polarity of sentiments expressed by users during disaster events have been widely researched. At aIdentifying the polarity of sentiments expressed by users during disaster events have been widely researched. At a recent time, social media has been successfully used as a proxy to gauge the impacts of disasters in real-time. With the growing of microblog sites on the Web, people have begun to express their opinions and emotions on a wide variety of topics on Twitter and other similar social services. We proposed a visual emotion analysis framework for natural disasters. The proposed framework consists of two components, emotion analysis modeling and geographic visualization. This emotion analysis modeling is mostly targeted in case of determining the emotions of Twitter users pre, peri and post natural disasters to help first responders for better managing the situations such as mental health of survived victims and fund raising after severe natural disasters. This geographic visualization system can help people for better understanding the changes of emotion reactions along with the duration of natural disasters and mostly interested regions of Twitter users on these natural disasters. In this research, the situations in California Fire which is happened in 2018 November is experimented for emotion analysis because the affected people often show their states and emotions via big data social media environment. recent time, social media has been successfully used as a proxy to gauge the impacts of disasters in real-time. With the growing of microblog sites on the Web, people have begun to express their opinions and emotions on a wide variety of topics on Twitter and other similar social services. We proposed a visual emotion analysis framework for natural disasters. The proposed framework consists of two components, emotion analysis modeling and geographic visualization. This emotion analysis modeling is mostly targeted in case of determining the emotions of Twitter users pre, peri and post natural disasters to help first responders for better managing the situations such as mental health of survived victims and fund raising after severe natural disasters. This geographic visualization system can help people for better understanding the changes of emotion reactions along with the duration of natural disasters and mostly interested regions of Twitter users on these natural disasters. In this research, the situations in California Fire which is happened in 2018 November is experimented for emotion analysis because the affected people often show their states and emotions via big data social media environment.},
 pages = {85--90},
 title = {Classification of People’s Emotions during Natural Disasters},
 volume = {49},
 year = {2020}
}