{"created":"2025-01-18T23:42:45.621680+00:00","updated":"2025-01-21T13:37:24.704123+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00095752","sets":["1164:2592:7086:7297"]},"path":["7297"],"owner":"11","recid":"95752","title":["マイクロクラスタリングを用いた単語分類とトピック検知"],"pubdate":{"attribute_name":"公開日","attribute_value":"2013-10-30"},"_buckets":{"deposit":"c7872d15-cad5-4467-bc22-d1f242388f4b"},"_deposit":{"id":"95752","pid":{"type":"depid","value":"95752","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"マイクロクラスタリングを用いた単語分類とトピック検知","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"マイクロクラスタリングを用いた単語分類とトピック検知"},{"subitem_title":"Topic detection using Micro Clustering","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2013-10-30","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"関西大学データマイニング応用研究センター"},{"subitem_text_value":"国立情報学研究所情報学プリンシプル研究系"},{"subitem_text_value":"関西学院大学経営戦略研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Data Mining Applied Research Center, Kwansei Gakuin University","subitem_text_language":"en"},{"subitem_text_value":"Principles of Informatics Research Division, National Institute of Informatics","subitem_text_language":"en"},{"subitem_text_value":"Institute of Business and Accounting, Kwansei Gakuin 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/95752/files/IPSJ-AL13145027.pdf"},"date":[{"dateType":"Available","dateValue":"2015-10-30"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-AL13145027.pdf","filesize":[{"value":"1.3 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":"9"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"bf8f57a6-a8d5-4c3e-a539-528a66850301","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2013 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"中原, 孝信"},{"creatorName":"宇野, 毅明"},{"creatorName":"羽室, 行信"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Takanobu, Nakahara","creatorNameLang":"en"},{"creatorName":"Takeaki, Uno","creatorNameLang":"en"},{"creatorName":"Yukinobu, Hamuro","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN1009593X","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":"本研究は，Twitter の投稿内容に，データ研磨技術を用いたマイクロクラスタリングを利用することで，単語の共起関係に基づいたクラスタによる概念を構築する．そして興味対象となるツイートをできる限り多く被覆するような少数のクラスタを，ナップサック制約付き最大被覆問題を用いて抽出することで，投稿内容の要約を行う．抽出されたクラスタは，ある特定のツイート群の文章を特徴付ける単語のグループとして捉えることができ，それらを概念として扱う事で，単語を独立に扱った場合に比べて，すぐれた要約になっていることを示す．計算実験では，テレビアニメーション番組「宇宙兄弟」に関する投稿内容を対象にして提案手法を適用した．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This research proposes a method to detect the contents of Twitter posts by analyzing the contents of tweets posted by viewers watching a specific TV program whenever the number of posts increase dramatically and then to summarize that content. First the proposed method creates concepts from clusters based on the co-occurrence of words. Then posts during tweet bursts are taken to be tweets of interest, and a minimal number of clusters that cover as much as possible those tweets are extracted using a knapsack-constrained maximum covering problem. A computational experiment shows the effectiveness of the proposed method with reference to a TV animation program “Space Brothers.”","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告アルゴリズム（AL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2013-10-30","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"27","bibliographicVolumeNumber":"2013-AL-145"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":95752,"links":{}}