{"created":"2025-01-19T01:29:49.761776+00:00","updated":"2025-01-19T11:14:39.063295+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230209","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"230209","title":["SNSを用いた株価の騰落予測におけるツイート抽出方法の比較検証"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"7a9e07ca-d3cd-412a-ab66-d06b02b5916a"},"_deposit":{"id":"230209","pid":{"type":"depid","value":"230209","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"SNSを用いた株価の騰落予測におけるツイート抽出方法の比較検証","author_link":["619372","619373"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"SNSを用いた株価の騰落予測におけるツイート抽出方法の比較検証"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2023-02-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"明大"},{"subitem_text_value":"明大"}]},"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/230209/files/IPSJ-Z85-6W-01.pdf","label":"IPSJ-Z85-6W-01.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-6W-01.pdf","filesize":[{"value":"306.7 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"2837cf85-e6f2-49ce-b3f3-d700672f08f5","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"安田, 健士郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"櫻井, 義尚"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿はツイートを用いた日経平均株価の騰落予測において、最適なツイートの抽出手法を比較検証するものである。近年、TwitterなどのSNSのテキスト情報をネガポジ分析し、機械学習を用いて株価を予測する研究が行われている。先行研究では独自の説明変数や予測モデルを作成し、精度の比較検証がされていたが、データセットとなるツイートの抽出手法に関しては検証されていなかった。本研究ではツイートを抽出するキーワードとして「日経平均」と複数の経済単語を組み合わせた数パターンの抽出手法を作成し、騰落予測を行う時系列モデルに組み込み、予測精度を比較することで最適なキーワードによるツイートの抽出手法を明らかにする。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"848","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"847","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230209,"links":{}}