{"updated":"2025-01-20T08:46:39.701151+00:00","links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00169996","sets":["1164:4179:8454:8862"]},"path":["8862"],"owner":"11","recid":"169996","title":["複数時点の単語出現頻度を扱う時系列データモデリング"],"pubdate":{"attribute_name":"公開日","attribute_value":"2016-07-22"},"_buckets":{"deposit":"39e54a67-ae8b-4e97-be86-9a87fa3fb530"},"_deposit":{"id":"169996","pid":{"type":"depid","value":"169996","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"複数時点の単語出現頻度を扱う時系列データモデリング","author_link":["342278","342277","342276"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"複数時点の単語出現頻度を扱う時系列データモデリング"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Twitter分析","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2016-07-22","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"奈良先端科学技術大学院大学"},{"subitem_text_value":"奈良先端科学技術大学院大学"},{"subitem_text_value":"奈良先端科学技術大学院大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Nara Institute of Science and Technology","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/169996/files/IPSJ-NL16227017.pdf","label":"IPSJ-NL16227017.pdf"},"date":[{"dateType":"Available","dateValue":"2018-07-22"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-NL16227017.pdf","filesize":[{"value":"1.2 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"23"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"f305c99c-601d-496a-befa-b3486c55db34","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2016 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"磯, 颯"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"若宮, 翔子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"荒牧, 英治"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10115061","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":"2188-8779","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"ソーシャルメディアの普及に伴い，様々な情報がインターネット上で共有されている．この結果，様々な社会現象および自然現象をインターネット上の情報から把握できるようになっている．特に，感染症に関するサーベイランス （現状把握技術） は，かつてない即時性から大きく注目されている．本研究では，ソーシャルメディア上で話題として取り上げられることが最も多い感染症の一つであるインフルエンザを題材に，従来のような現状把握だけでなく，流行予測を行うことを目指す．まず，実際に感染症が流行する前に，感染症の予防に関する情報が共有されていることに注目し，インフルエンザの流行を早期に示すような語を自動的に検出する．次に，実際の流行と任意の単語の相互相関係数を計算し，適切な時間ギャップの分だけタイムシフトした単語頻度を用いてモデルを構築し，患者数の予測を行う．この相互相関係数によるタイムシフトは，現状予測モデルの自然な拡張であるととともに，インフルエンザのみならず，あらゆる感染症の予測に適応可能な手法である．2012 年 8 月から 2016 年 1 月までの，インフルエンザに関連する 770 万発言を用いた実験の結果，現状の患者数を相関係数平均 0.93 で推定し，1 週間先の患者数を相関係数平均 0.91，3 週間先の患者数を相関係数平均 0.76 で予測することができた．この結果は，現状推定については，本邦における最高精度である．予測については，初めての試みであり，今後の適応範囲の拡大，および，実用化が望まれる．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告自然言語処理（NL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2016-07-22","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"17","bibliographicVolumeNumber":"2016-NL-227"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":169996,"created":"2025-01-19T00:40:42.813161+00:00"}