{"created":"2021-03-01T06:16:56.900646+00:00","id":408,"links":{},"metadata":{"_buckets":{"deposit":"f89a2b59-f157-4720-acda-b6dcde59fd71"},"_deposit":{"id":"408","owners":[],"pid":{"revision_id":0,"type":"depid","value":"408"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00000408","sets":["34:95:96","9:10:15"]},"item_2_alternative_title_1":{"attribute_name":"その他のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"レビューに対する評価指標の自動付与"}]},"item_2_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2007-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicPageEnd":"296","bibliographicPageStart":"273","bibliographicVolumeNumber":"14","bibliographic_titles":[{"bibliographic_title":"自然言語処理"}]}]},"item_2_description_13":{"attribute_name":"フォーマット","attribute_value_mlt":[{"subitem_description":"application/pdf","subitem_description_type":"Other"}]},"item_2_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"We propose a novel type of document classification task that quantifies how much a given document (review) appreciates the target object by using a continuous measure called sentiment polarity score (SP score) rather than binary polarity (good or bad). An SP score gives a concise summary of a review, and provides more information than binary classification. The difficulty of this task lies in the quantification of polarity. In this paper we use support vector regression (SVR) to tackle this problem. Experiments on book reviews using five-point scales show that SVR outperforms a multi-class classification method using support vector machines, and the results are close to human performance. We also examine the effect of sentence subjectivity detection using a Naive Bayes classifier, and show that this improves the robustness of the classifier.","subitem_description_type":"Abstract"},{"subitem_description":"本論文では,ある対象を評価している文章(レビュー)が与えられた時,対象物に対する評価が｢良い｣か｢悪い｣かでレビューを二倍分類するのではなく,どの桂度｢良い｣か｢悪い｣かの指標(sentimentpolarityscore (SPscore))をレビューに与える新しいタスクを提案する.SPscoreはレビューの簡潔な要約であり,単純な｢良い｣か｢悪い｣かの二倍分類より詳細な情報を与える.このタスクの難しさは連続した量であるSPscoreをどのようにしてレビューから得られるかにある.本稿ではsupportvectorregressionを用いてSPscoreを求める方法を提案する.5段階評価がついた本に対するレビューを用いた実験で,我々の手法がsupportvectormachinesを用いた多値分類より高い精度であり,人による指標の予測結果に近いことを示す.また,NaiveBayesClassifierを用いた文単位での主観性分析を用いることにより我々の手法の頑健性が増すことを示す.","subitem_description_type":"Abstract"}]},"item_2_full_name_3":{"attribute_name":"著者別名","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"106315","nameIdentifierScheme":"WEKO"}],"names":[{"name":"岡野原, 大輔"}]},{"nameIdentifiers":[{"nameIdentifier":"106316","nameIdentifierScheme":"WEKO"}],"names":[{"name":"辻井, 潤一"}]}]},"item_2_publisher_20":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"言語処理学会"}]},"item_2_source_id_10":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10472659","subitem_source_identifier_type":"NCID"}]},"item_2_source_id_8":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"13407619","subitem_source_identifier_type":"ISSN"}]},"item_2_subject_15":{"attribute_name":"日本十進分類法","attribute_value_mlt":[{"subitem_subject":"007","subitem_subject_scheme":"NDC"}]},"item_2_text_21":{"attribute_name":"出版者別名","attribute_value_mlt":[{"subitem_text_value":"The Association for Natural Language Processing"}]},"item_2_text_4":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学情報理工学系研究科コンピュータ科学専攻"},{"subitem_text_value":"Graduate school of Information Science and Technology, University of Tokyo"},{"subitem_text_value":"NaCTeM (National Center for Text Mining), University of Manchester"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"OKANOHARA, DAISUKE"}],"nameIdentifiers":[{"nameIdentifier":"106313","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"TSUJI, JUN'ICHI"}],"nameIdentifiers":[{"nameIdentifier":"106314","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":"2017-06-26"}],"displaytype":"detail","filename":"v14n3_15.pdf","filesize":[{"value":"1.6 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"v14n3_15.pdf","url":"https://repository.dl.itc.u-tokyo.ac.jp/record/408/files/v14n3_15.pdf"},"version_id":"5dda2f63-1fc3-4bfa-89c1-3b4e0c19ad0f"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Sentiment Analysis","subitem_subject_scheme":"Other"},{"subitem_subject":"Document Classification","subitem_subject_scheme":"Other"},{"subitem_subject":"Machine Learning","subitem_subject_scheme":"Other"},{"subitem_subject":"評判分析","subitem_subject_scheme":"Other"},{"subitem_subject":"文章分類","subitem_subject_scheme":"Other"},{"subitem_subject":"機械学","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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":"Assigning Polarity Scores to Reviews Using Machine Learning Techniques","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Assigning Polarity Scores to Reviews Using Machine Learning Techniques"}]},"item_type_id":"2","owner":"1","path":["15","96"],"pubdate":{"attribute_name":"公開日","attribute_value":"2009-12-08"},"publish_date":"2009-12-08","publish_status":"0","recid":"408","relation_version_is_last":true,"title":["Assigning Polarity Scores to Reviews Using Machine Learning Techniques"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-12-19T03:41:14.255086+00:00"}