{"created":"2021-03-01T06:20:12.146383+00:00","id":3558,"links":{},"metadata":{"_buckets":{"deposit":"f839e422-fe8d-4aea-86ea-96dba5ee984d"},"_deposit":{"id":"3558","owners":[],"pid":{"revision_id":0,"type":"depid","value":"3558"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00003558","sets":["110:203:257","9:233:234"]},"item_7_alternative_title_1":{"attribute_name":"その他のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"Analysis of Human Behavior of a User using Geotag of Microblog"}]},"item_7_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2012-03-22","bibliographicIssueDateType":"Issued"},"bibliographic_titles":[{}]}]},"item_7_date_granted_25":{"attribute_name":"学位授与年月日","attribute_value_mlt":[{"subitem_dategranted":"2012-03-22"}]},"item_7_degree_name_20":{"attribute_name":"学位名","attribute_value_mlt":[{"subitem_degreename":"修士(環境学)"}]},"item_7_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"リアル・ワールドとWebとの距離が近くなる中で, Webからリアル・ワールドの情報を抽出する技術に注目が集まっている. 本研究では, 現在世界中で広く使われるようになったTwitterに注目し, Twitterとジオタグを用いることでユーザのリアル・ワールドでの日常的な行動と, 行動の意図を抽出する. ただし, Tweet Dataは膨大になるため, クラスタリングすることでデータを処理する. ジオタグ付きTweetは, 投稿位置と投稿時間, 投稿内容を属性として持っており, これらの情報を利用し時空間＋テキストでクラスタリングを行い, ユーザの日常的に活動している地点を推定する. また, 投稿内容を基にした位置へのラベリングにより, ユーザの行動について推定する. 評価実験では, 位置情報だけでなく, 時間やキストを利用することで, より精度の良いクラスタリングができることを確認した. ただし, ユーザによって重視すべき項目が異なり, より精度を上げてクラスタリングを行うためにはユーザの分類が必要であると考えられる. また, 700以上のジオタグ付きTweetがあれば, ユーザの行動推定をある程度の誤差で行うことができることを示した. より精度よくユーザの行動を抽出する手法の検討, そしてユーザの行動情報を利活用するアプリケーションの提案が課題として考えられる.","subitem_description_type":"Abstract"},{"subitem_description":"Recently, with the advent of portable devices such as smartphones, \"context-aware services\" that support the work and everyday life are greatly expected. Context-aware services are essential to understand the user's every 24 hours behavior. I focus on Twitter now widely used around the world for analysis of user behavior context. \"Geotag\", a feature of Twitter, stores the user behavior history so we can analyze user behavior context. In this study, geotag from Twitter is used to estimate the scope and patterns of user mobility. I aim to analyze user behavior context with clustering based on time, location and text of geotagged Tweets. Clusters labeling is based on the contents of Tweets as well. In the experiment, it was confirmed that clustering can be more accurate using time and text content than criteria. When using time and text, average error improve 100 meter comparing with using only location. As a result, using 700 or more geotagged Tweets, it is possible to estimate user behavior with some degree of error. In futre work, I try to improve the precision of clustering and labeling, and make some application which use human behavior data from my method.","subitem_description_type":"Abstract"}]},"item_7_full_name_3":{"attribute_name":"著者別名","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"8491","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Sakamaki, Tomohiro"}]}]},"item_7_select_21":{"attribute_name":"学位","attribute_value_mlt":[{"subitem_select_item":"master"}]},"item_7_subject_13":{"attribute_name":"日本十進分類法","attribute_value_mlt":[{"subitem_subject":"007","subitem_subject_scheme":"NDC"}]},"item_7_text_24":{"attribute_name":"研究科・専攻","attribute_value_mlt":[{"subitem_text_value":"新領域創成科学研究科環境学研究系社会文化環境学専攻"}]},"item_7_text_27":{"attribute_name":"学位記番号","attribute_value_mlt":[{"subitem_text_value":"修創域第4454号"}]},"item_7_text_4":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学大学院新領域創成科学研究科環境学研究系社会文化環境学専攻"},{"subitem_text_value":"Department of Socio-Cultural Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"酒巻, 智宏"}],"nameIdentifiers":[{"nameIdentifier":"8490","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":"2017-05-31"}],"displaytype":"detail","filename":"K-03439.pdf","filesize":[{"value":"10.4 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"本文(fulltext)","url":"https://repository.dl.itc.u-tokyo.ac.jp/record/3558/files/K-03439.pdf"},"version_id":"b3828b24-ee40-4384-8232-d293f8993164"},{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2017-05-31"}],"displaytype":"detail","filename":"K-03439-a.pdf","filesize":[{"value":"381.7 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"要旨(summary)","url":"https://repository.dl.itc.u-tokyo.ac.jp/record/3558/files/K-03439-a.pdf"},"version_id":"da952a1b-f74d-47c0-b2ad-89a0ec4c8876"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"マイクロブログ","subitem_subject_scheme":"Other"},{"subitem_subject":"Twitter","subitem_subject_scheme":"Other"},{"subitem_subject":"ジオタグ","subitem_subject_scheme":"Other"},{"subitem_subject":"行動調査","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":"thesis","resourceuri":"http://purl.org/coar/resource_type/c_46ec"}]},"item_title":"マイクロブログのジオタグを用いたユーザの行動分析","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"マイクロブログのジオタグを用いたユーザの行動分析"}]},"item_type_id":"7","owner":"1","path":["234","257"],"pubdate":{"attribute_name":"公開日","attribute_value":"2012-10-11"},"publish_date":"2012-10-11","publish_status":"0","recid":"3558","relation_version_is_last":true,"title":["マイクロブログのジオタグを用いたユーザの行動分析"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-12-19T03:45:15.515463+00:00"}