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  1. 工学部
  2. 紀要掲載論文 (工学部)
  3. 宮崎大學工學部紀要
  4. 49号

Classification of People’s Emotions during Natural Disasters

http://hdl.handle.net/10458/00010078
http://hdl.handle.net/10458/00010078
73b1b8f5-02b5-43f5-a3c3-5f16222124b1
名前 / ファイル ライセンス アクション
Engineering_49_p85.pdf 本文 (1.8 MB)
Item type 紀要論文 / Departmental Bulletin Paper(1)
公開日 2020-10-30
タイトル
タイトル Classification of People’s Emotions during Natural Disasters
言語 en
言語
言語 eng
キーワード
言語 en
主題Scheme Other
主題 Emotion analysis
キーワード
言語 en
主題Scheme Other
主題 Social media visual analytics
キーワード
言語 en
主題Scheme Other
主題 Natural disaster
キーワード
言語 en
主題Scheme Other
主題 Twitter
キーワード
言語 en
主題Scheme Other
主題 California fire
キーワード
言語 en
主題Scheme Other
主題 Support vector machine
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ departmental bulletin paper
著者 Nann, Hwan Khun

× Nann, Hwan Khun

WEKO 33075

en Nann, Hwan Khun

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Thi, Thi Zin

× Thi, Thi Zin

WEKO 33076

en Thi, Thi Zin

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Yokota, Mitsuhiro

× Yokota, Mitsuhiro

WEKO 7103
e-Rad 40191506

en Yokota, Mitsuhiro

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Hninn, Aye Thant

× Hninn, Aye Thant

WEKO 33078

en Hninn, Aye Thant

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抄録
内容記述タイプ Abstract
内容記述 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.
言語 en
書誌情報 ja : 宮崎大学工学部紀要
en : Memoirs of Faculty of Engineering, University of Miyazaki

巻 49, p. 85-90, 発行日 2020-09
出版者
出版者 宮崎大学工学部
言語 ja
出版者
出版者 Faculty of Engineering, University of Miyazaki
言語 en
ISSN
収録物識別子タイプ ISSN
収録物識別子 05404924
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AA00732558
著者版フラグ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
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