{"updated":"2025-01-19T14:56:00.263852+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00219077","sets":["934:989:10777:10959"]},"path":["10959"],"owner":"44499","recid":"219077","title":["画像投稿SNSにおけるハッシュタグの投稿関連度予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-07-26"},"_buckets":{"deposit":"bac2cd47-0c38-49e0-b554-8ff299a21f89"},"_deposit":{"id":"219077","pid":{"type":"depid","value":"219077","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"画像投稿SNSにおけるハッシュタグの投稿関連度予測","author_link":["571073","571072","571074","571075"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"画像投稿SNSにおけるハッシュタグの投稿関連度予測"},{"subitem_title":"Predicting Post Relevance of Hashtags in Image Posting SNS","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[事例紹介論文] SNS，ハッシュタグ，逆共起カウント数，逆共起ランキング値，コメント間類似度","subitem_subject_scheme":"Other"}]},"item_type_id":"3","publish_date":"2022-07-26","item_3_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋大学大学院情報学研究科"},{"subitem_text_value":"名古屋大学大学院情報学研究科"}]},"item_3_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Informatics, Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Informatics, Nagoya 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/219077/files/IPSJ-TOM1503010.pdf","label":"IPSJ-TOM1503010.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-26"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-TOM1503010.pdf","filesize":[{"value":"1.4 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":"17"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"7225b6c3-7a10-4b6a-9d57-81002f1196de","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_3_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"神野, 悦太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"北, 栄輔"}],"nameIdentifiers":[{}]}]},"item_3_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Etsutaro, Kamino","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Eisuke, Kita","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_3_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11464803","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_3_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7780","subitem_source_identifier_type":"ISSN"}]},"item_3_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"画像投稿SNSに記事を投稿する際には，投稿に大量のハッシュタグを付加することが多い．記事に対して適切なハッシュタグを自力で探すのは難しく，時間のかかる作業であるため，ハッシュタグを自動で推薦するシステムが作成されてきた．従来のシステムでは，ハッシュタグをキーワードとして検索して取得した共起ハッシュタグを用いて推薦を行うため，投稿と直接関係のないハッシュタグを複数推薦してしまうという問題があった．そこで本研究では，ハッシュタグごとにInstagramから取得した共起ハッシュタグの共起数ランキング上位のものに対して，逆共起カウント数，逆共起ランキング値，およびコメント間類似度を適用してハッシュタグとの関連度を評価する数理モデルを提案する．説明変数とキーワードとの関連度の正解値は，実際のInstagramユーザに対して行ったアンケート調査の結果から求めて使用する．判別アルゴリズムとして，サポートベクターマシン（SVM）とランダムフォレストを比較した結果，ランダムフォレストが最も高い判別精度を示すことが分かった．","subitem_description_type":"Other"}]},"item_3_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In posting images on social networking services (SNS), a large number of hashtags are often added to the posts. Since searching for hashtags by oneself is a difficult and time-consuming task, systems that automatically recommend hashtags have been created. Conventional systems use co-occurring hashtags obtained by searching for hashtags as keywords to make recommendations, which leads to the problem of recommending multiple hashtags that are not directly related to the post. For solving this problem, a new mathematical model to evaluate the relevance of hashtags by applying the reverse co-occurrence count, the reverse co-occurrence ranking value, and the similarity between comments to the top co-occurrence rankings of co-occurring hashtags are proposed in this study. The relevance between keywords and the variables is derived from the results of a questionnaire survey conducted among actual Instagram users. Support Vector Machine (SVM) and Random Forest are compared in the accuracy of the discriminant analysis. The results show that the Random Forest shows the best accuracy among them. ","subitem_description_type":"Other"}]},"item_3_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"105","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌数理モデル化と応用（TOM）"}],"bibliographicPageStart":"97","bibliographicIssueDates":{"bibliographicIssueDate":"2022-07-26","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"15"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:19:24.731674+00:00","id":219077,"links":{}}