{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00219970","sets":["1164:1165:10997:10998"]},"path":["10998"],"owner":"44499","recid":"219970","title":["被災者の感情分類に基づく有益な行動促進情報の分析"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-09-02"},"_buckets":{"deposit":"6b64b504-455a-429c-b2a9-9fb95ea826f0"},"_deposit":{"id":"219970","pid":{"type":"depid","value":"219970","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"被災者の感情分類に基づく有益な行動促進情報の分析","author_link":["574565","574561","574564","574563","574566","574562"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"被災者の感情分類に基づく有益な行動促進情報の分析"},{"subitem_title":"Extraction of Behavioral Facilitation Information based on Sentiments","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2022-09-02","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"甲南大学大学院自然科学研究科"},{"subitem_text_value":"千葉工業大学情報科学部"},{"subitem_text_value":"甲南大学大学院自然科学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Konan University","subitem_text_language":"en"},{"subitem_text_value":"Chiba Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"Konan University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/219970/files/IPSJ-DBS22175017.pdf","label":"IPSJ-DBS22175017.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DBS22175017.pdf","filesize":[{"value":"990.6 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"13"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"8733cd7b-d63e-43d0-8465-c47c56a7a43f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"山本, 楓登"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"熊本, 忠彦"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"灘本, 明代"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Futo, Yamamoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tadahiko, Kumamoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akiyo, Nadamoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10112482","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-871X","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"災害発生時の Twitter 上には閲覧した被災者ユーザに行動を促すような情報（行動促進情報）が数多く投稿されるが，有益な情報もあれば非有益な情報もある．これまで我々は，災害時に被災者が抱くネガティブな感情に焦点を当て，被災者の感情に合った有益な行動促進情報の特徴を分析してきた．具体的には，被災者のネガティブな感情を分析することにより「おびえ・恐怖・不安」，「心配」，「困っている」，「不快」という 4 種類の感情軸を定義し，各々の感情軸において有益な行動促進情報の特徴（名詞の出現状況）を分析してきた．本論文では，それぞれの感情軸において有益な行動促進ツイートか非有益な行動促進ツイートかを自動分類する BERT（Bidirectional Encoder Representations from Transformers）ベースの手法を提案するとともに，各感情軸に分類されたツイートの文末表現とトピッククラスタリングにより得られたクラスタごとの特徴語（名詞）を分析することで，感情軸ごとに有益な行動促進ツイートの特徴をより詳細に明らかにする．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告データベースシステム（DBS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-09-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"17","bibliographicVolumeNumber":"2022-DBS-175"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":219970,"updated":"2025-01-19T14:42:49.746658+00:00","links":{},"created":"2025-01-19T01:20:01.381666+00:00"}