転移学習を用いたSNSにおける感情分析の精度向上と炎上検知への応用

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): Improving the Accuracy of Sentiment Analysis of SNS Comments Using Transfer Learning and Its Application to Flaming Detection

著者名: 吉田　舜（神戸大学大学院工学研究科），北園　淳（神戸大学大学院工学研究科），小澤　誠一（神戸大学大学院工学研究科），菅原　貴弘（(株)エルテス），芳賀　達也（(株)エルテス）

著者名(英語): Shun Yoshida (Graduate School of Engineering, Kobe University), Jun Kitazono (Graduate School of Engineering, Kobe University), Seiichi Ozawa (Graduate School of Engineering, Kobe University), Takahiro Sugawara (Eltes Co., Ltd.), Tatsuya Haga (Eltes Co., Ltd.)

キーワード: SNS，感情分析，炎上検知，転移学習　　SNS，sentiment analysis，flaming detection，transfer learning

要約(英語): In recent years, along with the popularization of SNS, the incidents, which are called flaming, that the number of negative comments surges are on the increase. This becomes a problem for companies because flamings hurt companies' reputation. In order to minimalize the damage of reputation, we propose the method that detects flamings by estimating the sentiment polarities of SNS comments. Because of the unique SNS characteristics such as repetition of same comments, the polarities of words are sometimes wrongly estimated. To alleviate this problem, transfer learning is introduced. In this research, the sentiment polarities of words are trained in every domain. This will enable to extract the words that are domain-specific and dictate the polarity of comments. These words are occurred in retweets. Transfer learning is implemented to non-extracted words by averaging the occurrence probabilities in other domains. These processes keep the polarities of important words that dictate the polarity of comments and modify the wrongly estimated polarities of words. The experimental results show that the proposed method improves the performance of estimating the sentiment polarity of comments. Moreover, flamings can be detected without missing by monitoring time course of the number of negative comments.

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: 340-347 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_340/_article/-char/ja/