異常値検出を用いたグループ固有なテキストの発見

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2016/03/01

タイトル(英語): Group Specific Text Discovery Using Abnormal Detection

著者名: 柳本　豪一（大阪府立大学大学院 工学研究科），伊佐治　俊（NTTドコモ）

著者名(英語): Hidekazu Yanagimoto (Osaka Prefecture University), Suguru Isaji (NTT Docomo)

キーワード: 自然言語処理，密度比推定，ソーシャルメディア解析　　Natural language processing，Density ratio estimation，Social media analysis

要約(英語): In social media many users send personal messages depending on their environments and such messages are used as outputs from a sensor system observing the real world. But the social media is quite different from a general sensor system because users regarded as sensors make messages based on various judgement criteria and the criteria is not controllable. In this study we assume that the judgement criteria occurs according to their belonging communities. So we try to extract messages emphasizing the difference of communities. In this paper we proposed a group specific text discovery method using abnormal detection. We use Twitter as messages generated by social media users. Because the tweets include description of events and tweet generated location, we can extract characteristic tweets based on their generated location. In an evaluation experiment we used tweets related to heavy snow in Yamanashi and found some messages describing local information comparing with tweets except Yanamashi.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.136 No.3 （2016） 特集：機械学習が拓くシステムイノベーション

本誌掲載ページ: 327-332 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/136/3/136_327/_article/-char/ja/