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ID 113915
著者
Bando, Koji NTT Plala
北, 研二 ATR Interpreting Telephony Research Laboratories 徳島大学 教育研究者総覧 KAKEN研究者をさがす
キーワード
Hobby estimation
deep neural networks
sequential statements
social media
資料タイプ
学術雑誌論文
抄録
With more and more frequency, users communicate with each other on social media. Many users start on Twitter or Facebook to find friends who have the same hobby. Our study proposes a method to estimate the users’ interests (hobby) based on tweets on Twitter. One tweet does not, in and of itself, contain a lot of information, and some tweets are not related to the user’s hobby. Therefore, we propose a reliable hobby estimation method by extracting features from multiple, sequential tweets. The proposed method uses Recurrent Neural Networks (RNN) which can accommodate time-series information. We also used a Convolutional Neural Networks (CNN) which can treat contextual information. We used an averaged vector of word distributed representation as a feature. Using the proposed method based on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), we obtained a 23.72% improvement as compared with a baseline method using a Random Forest (RF) regression as a machine learning algorithm.
掲載誌名
International Journal of Machine Learning and Computing
ISSN
20103700
9
2
開始ページ
108
終了ページ
114
発行日
2019-04
権利情報
This article is licensed under an open access Creative Commons CC BY 4.0 license(https://creativecommons.org/licenses/by/4.0/), which means all papers can be downloaded, shared, and reused without restriction, as long as the original authors are properly cited.
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出版社版DOI
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言語
eng
著者版フラグ
出版社版
部局
理工学系