{"_buckets": {"deposit": "5110c7d2-7e3b-4bb6-aed2-a47b209ce663"}, "_deposit": {"id": "18757", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "18757"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00018757", "sets": ["322"]}, "author_link": ["54711", "54712", "54713", "54714", "54715", "54716"], "item_10_alternative_title_19": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "A Proposal of Online Topic Model for Twitter : Considering Temporal Dynamics of User Interests and Topic Trends", "subitem_alternative_title_language": "en"}]}, "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2013-09", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "3", "bibliographicPageEnd": "6", "bibliographicPageStart": "1", "bibliographicVolumeNumber": "2013-MPS-95", "bibliographic_titles": [{"bibliographic_title": "情報処理学会論文誌: MPS, 数理モデル化と問題解決研究報告", "bibliographic_titleLang": "ja"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "Latent Dirichlet Allocation (LDA) は,様々な分野で応用されているトピックモデルであり,Twitter におけるユーザ属性の推定や話題の要約などに適用した研究も数多く報告され始めている.LDA をツイート集合に適用する場合,1 ツイートを 1 文書とすると,文書の短さやノイズの多さにより,LDA が有効に機能しないことが多いため,1 ユーザの全ツイートを 1 文書とする方法が一般的に用いられる.これに対して,1 ツイートが 1 トピックから成るという仮定に基づいたトピックモデルである Twitter-LDA が提案され,前者の方法に比べて,トピックの意味のまとまりの面で優れていると報告されている.しかし一方で Twitter-LDA は,オンライン学習ができないという課題がある.本論文では,Twitter-LDA を改良し,Twitter に適したオンライン学習可能なトピックモデルを提案する.提案モデルでは以下の二点について Twitter-LDA を拡張する.第一に,一般語とトピック語との比率をユーザごとに推定することで,より高精度にツイートの生成過程をモデル化する.第二に,ユーザの購買行動をモデル化した Topic Tracking Model (TTM) の機構をモデルに加えることで,Twitter におけるユーザの興味と話題の時間発展をオンラインで学習可能とする.", "subitem_description_language": "ja", "subitem_description_type": "Abstract"}, {"subitem_description": "Latent Dirichlet Allocation (LDA) is a topic model which has been applied to various fields. It has been also applied to user profiling or event summarization on Twitter. In the application of LDA to tweet collection, it generally treats aggregated all tweets of a user as a single document. On the other hand, Twitter-LDA which assumes a single tweet consists of a single topic has been proposed and showed that it is superior to the former way in topic semantic coherence. However, Twitter-LDA has a problem that it is not capable of online inference. In this paper, we extend Twitter-LDA in the following two points. First, we model the generation process of tweets more accurately by estimating the ratio between topic words and general words for each user. Second, we enable it to estimate temporal dynamics of user interests and topic trends in online based on Topic Tracking Model (TTM) which models consumer purchase behaviors.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "URI", "subitem_identifier_uri": "http://id.nii.ac.jp/1001/00095217/"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/20853"}]}, "item_10_publisher_32": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "一般社団法人情報処理学会", "subitem_publisher_language": "ja"}]}, "item_10_relation_43": {"attribute_name": "関連情報", "attribute_value_mlt": [{"subitem_relation_type": "isVersionOf", "subitem_relation_type_id": {"subitem_relation_type_id_text": "http://id.nii.ac.jp/1001/00095217/", "subitem_relation_type_select": "URI"}}]}, "item_10_rights_12": {"attribute_name": "権利", "attribute_value_mlt": [{"subitem_rights": "ここに掲載した著作物の利用に関する注意 本著作物の著作権は情報処理学会に帰属します。本著作物は著作権者である情報処理学会の許可のもとに掲載するものです。ご利用に当たっては「著作権法」ならびに「情報処理学会倫理綱領」に従うことをお願いいたします。Notice for the use of this material The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). This material is published on this web site with the agreement of the author (s) and the IPSJ. Please be complied with Copyright Law of Japan and the Code of Ethics of the IPSJ if any users wish to reproduce, make derivative work, distribute or make available to the public any part or whole thereof. All Rights Reserved, Copyright (C) Information Processing Society of Japan. Comments are welcome. Mail to address editj＠ipsj.or.jp, please.", "subitem_rights_language": "ja"}]}, "item_10_select_15": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_source_id_7": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "0919-6072", "subitem_source_identifier_type": "PISSN"}]}, "item_1615787544753": {"attribute_name": "出版タイプ", "attribute_value_mlt": [{"subitem_version_resource": "http://purl.org/coar/version/c_970fb48d4fbd8a85", "subitem_version_type": "VoR"}]}, "item_access_right": {"attribute_name": "アクセス権", "attribute_value_mlt": [{"subitem_access_right": "open access", "subitem_access_right_uri": "http://purl.org/coar/access_right/c_abf2"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "佐々木, 謙太朗", "creatorNameLang": "ja"}], "nameIdentifiers": [{"nameIdentifier": "54711", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "吉川, 大弘", "creatorNameLang": "ja"}], "nameIdentifiers": [{"nameIdentifier": "54712", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "古橋, 武", "creatorNameLang": "ja"}], "nameIdentifiers": [{"nameIdentifier": "54713", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "SASAKI, KENTARO", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "54714", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "YOSHIKAWA, TOMOHIRO", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "54715", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "FURUHASHI, TAKESHI", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "54716", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-21"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "43_A_Proposal_of_Online_Topic_Model_for_Twitter.pdf", "filesize": [{"value": "1.3 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_note", "mimetype": "application/pdf", "size": 1300000.0, "url": {"label": "43_A_Proposal_of_Online_Topic_Model_for_Twitter.pdf", "objectType": "fulltext", "url": "https://nagoya.repo.nii.ac.jp/record/18757/files/43_A_Proposal_of_Online_Topic_Model_for_Twitter.pdf"}, "version_id": "fbd3ed18-c1a2-4b82-ad7f-2cd58faba273"}]}, "item_keyword": {"attribute_name": "キーワード", "attribute_value_mlt": [{"subitem_subject": "トピックモデル", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Twitter", "subitem_subject_scheme": "Other"}, {"subitem_subject": "時間発展", "subitem_subject_scheme": "Other"}, {"subitem_subject": "オンライン学習", "subitem_subject_scheme": "Other"}, {"subitem_subject": "topic model", "subitem_subject_scheme": "Other"}, {"subitem_subject": "time evolution", "subitem_subject_scheme": "Other"}, {"subitem_subject": "online learning", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "Twitterにおけるユーザの興味と話題の時間発展を考慮したオンライン学習可能なトピックモデルの提案", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Twitterにおけるユーザの興味と話題の時間発展を考慮したオンライン学習可能なトピックモデルの提案", "subitem_title_language": "ja"}]}, "item_type_id": "10", "owner": "1", "path": ["322"], "permalink_uri": "http://hdl.handle.net/2237/20853", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2014-11-21"}, "publish_date": "2014-11-21", "publish_status": "0", "recid": "18757", "relation": {}, "relation_version_is_last": true, "title": ["Twitterにおけるユーザの興味と話題の時間発展を考慮したオンライン学習可能なトピックモデルの提案"], "weko_shared_id": -1}