{"created":"2023-05-15T14:25:31.744623+00:00","id":8206,"links":{},"metadata":{"_buckets":{"deposit":"96f77f5d-f3f6-47e8-bd39-2fd3631c75d6"},"_deposit":{"created_by":17,"id":"8206","owners":[17],"pid":{"revision_id":0,"type":"depid","value":"8206"},"status":"published"},"_oai":{"id":"oai:bunkyo.repo.nii.ac.jp:00008206","sets":["1:35:924"]},"author_link":["9916","319"],"control_number":"8206","item_5_biblio_info_13":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2023-03-31","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"10","bibliographicPageStart":"1","bibliographicVolumeNumber":"45","bibliographic_titles":[{"bibliographic_title":"生活科学研究","bibliographic_titleLang":"ja"},{"bibliographic_title":"Bulletin of Living Sciences","bibliographic_titleLang":"en"}]}]},"item_5_date_43":{"attribute_name":"作成日","attribute_value_mlt":[{"subitem_date_issued_datetime":"2022-03-31","subitem_date_issued_type":"Created"}]},"item_5_description_12":{"attribute_name":"内容記述","attribute_value_mlt":[{"subitem_description":"本研究の目的は、ツイート頻度の客観性を担保するために、ツイートを一つの単位としたテキストデータの分析を行うこと。また、なるべく多くの情報を捨象せずにテキストデータを数量化し、感情変数との関連を検討することで、探索的にツイート内容とネガティブ感情の関連を検討すること、およびその手法の提案である。25 人の過去2 週間のツイート（234 ツイート）をラベル化し、KJ 法式のグループ編成とカテゴリカル主成分分析およびクラスター分析を用いてグルーピングを行った。その結果、【イベントツイート】【情緒的ツイート】【ユーモアツイート】の３つが生成された。非線形正準相関分析により、大学生用ストレス反応尺度（情動反応）「抑うつ」「不安」「怒り」との関連を検討したところ、【ユーモアツイート】と「抑うつ」「不安」の関連，および【情緒的ツイート】と「怒り」の関連が示唆された。","subitem_description_language":"ja","subitem_description_type":"Abstract"}]},"item_5_identifier_registration":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.15034/00008179","subitem_identifier_reg_type":"JaLC"}]},"item_5_publisher_16":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"文教大学","subitem_publisher_language":"ja"}]},"item_5_source_id_19":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"02852454","subitem_source_identifier_type":"PISSN"}]},"item_5_text_39":{"attribute_name":"本文言語","attribute_value_mlt":[{"subitem_text_value":"日本語"}]},"item_5_text_42":{"attribute_name":"ID","attribute_value_mlt":[{"subitem_text_value":"BKK0004392"}]},"item_5_text_8":{"attribute_name":"所属機関","attribute_value_mlt":[{"subitem_text_language":"ja","subitem_text_value":"文教大学人間科学部（非常勤講師）"},{"subitem_text_language":"ja","subitem_text_value":"文教大学人間科学部"}]},"item_5_version_type_35":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"堀切, 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法および最適尺度法を用いて","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"実際のツイートを収集して感情との関連を検討する方法の提案：KJ 法および最適尺度法を用いて","subitem_title_language":"ja"},{"subitem_title":"Proposal on a method to consider the relationship between actual tweets collected and emotions : By KJ method and Optimal Scaling","subitem_title_language":"en"}]},"item_type_id":"5","owner":"17","path":["924"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2023-03-31"},"publish_date":"2023-03-31","publish_status":"0","recid":"8206","relation_version_is_last":true,"title":["実際のツイートを収集して感情との関連を検討する方法の提案：KJ 法および最適尺度法を用いて"],"weko_creator_id":"17","weko_shared_id":-1},"updated":"2024-08-07T08:11:51.564347+00:00"}