2019 Fiscal Year Final Research Report
A Study on Integrated Kansei Information Mining Techniques for Individual Writers and Readers
Project/Area Number |
17K00430
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Web informatics, Service informatics
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Research Institution | Chiba Institute of Technology |
Principal Investigator |
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Co-Investigator(Kenkyū-buntansha) |
灘本 明代 甲南大学, 知能情報学部, 教授 (30359103)
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Project Period (FY) |
2017-04-01 – 2020-03-31
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Keywords | 感情 / 印象 / ツイート |
Outline of Final Research Achievements |
The purpose of this study is to extract three kinds of affective information from sentences such as tweets: emotions of the writers who wrote the sentences, impressions which readers felt from reading the sentences, and the writers' emotions which the readers estimated by reading the sentences. First, we conduct questionnaire surveys, and analyze the relationships between tweets and emotions and impressions extracted from the tweets. And then, we formulate these relationships using multivariate analysis methods and machine learning methods, resulting in high accuracy and robustness. We have developed a method that can extract the emotions and impressions of tweets. Note that, in this study, ten kinds of emotions of "joy, love, relief, sad, dislike, fear, anger, embarrassment, uplifting, and surprise" and eight kinds of impressions of "aggressive/unpleasant, negative, pleasant, pleasant/happy, positive, heartwarming, annoying, and scary" are used.
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Free Research Field |
感性情報処理
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Academic Significance and Societal Importance of the Research Achievements |
文章に内在する感性情報を抽出する研究において,書き手の感情,読み手の印象,読み手が推測する書き手の感情の3種類を同時に抽出し,相補的に利用するという研究は,これまでなかった.それぞれの感性情報を相補的に利用することで,より高精度な感性情報抽出(感性情報マイニング)を実現することが期待される.また.個々の書き手や読み手の感性情報をより高精度に抽出できるようになることで,既存の研究(トレンド分析やニュース記事推薦,リアルタイムイベント抽出,ユーザの印象選好の可視化,SNS等でのフォロイー推薦など)に与える影響も大きい
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