{"updated":"2025-01-21T11:20:34.405003+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00101214","sets":["1164:5159:7427:7570"]},"path":["7570"],"owner":"11","recid":"101214","title":["盛り上がり時間帯におけるツイートの言語的特性の解析"],"pubdate":{"attribute_name":"公開日","attribute_value":"2014-05-15"},"_buckets":{"deposit":"2c95e870-e612-42a1-b2d0-557e7961c85a"},"_deposit":{"id":"101214","pid":{"type":"depid","value":"101214","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"盛り上がり時間帯におけるツイートの言語的特性の解析","author_link":["0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"盛り上がり時間帯におけるツイートの言語的特性の解析"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"自然言語解析","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2014-05-15","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学／国立情報学研究所"},{"subitem_text_value":"国立情報学研究所"},{"subitem_text_value":"国立情報学研究所／お茶の水女子大学"},{"subitem_text_value":"東京大学／国立情報学研究所"}]},"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/101214/files/IPSJ-SLP14101003.pdf"},"date":[{"dateType":"Available","dateValue":"2016-05-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLP14101003.pdf","filesize":[{"value":"959.9 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"22"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"0a5c78d9-6675-4ad4-8043-e45fff76ba4c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2014 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"藤沼祥成"},{"creatorName":"横野光"},{"creatorName":"PascualMartinez-gomez"},{"creatorName":"相澤彰子"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10442647","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_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"あるイベントの盛り上がりに対して，それに関するツイートにも変化が現れその変化に着目することで盛り上がりを検出することが可能であると考えられる．本研究ではこの盛り上がり時間帯中のツイートに用いられている表現の特性を解析することを試みる．はじめに各時間帯のツイート集合とツイートより構築した言語モデルの関係をクロスエントロピーで算出した．実験結果より複数のハッシュタグ間における一部の盛り上がり時間帯のツイートはツイートより構築した n-gram 言語モデルに従うことを示す．また，盛り上がっている時間帯とそうでない時間帯において，クロスエントロピーにおいて統計的に有意差があることを示した (p＜0.02)．また，n-gram 言語モデルでは捉えられない素性も検討するため，Support Vector Machine (SVM) と Random Forest により各ツイートを盛り上がり時間帯の二値分類を行い，盛り上がり時間帯の特徴として漢字数が少ないことが明らかになった．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告音声言語情報処理（SLP）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2014-05-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"2014-SLP-101"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-18T23:46:51.274086+00:00","id":101214,"links":{}}