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ID 114311
著者
土屋, 誠司 Doshisha University
Kojima, Takumi Tokushima University
Kondo, Hiroya Sharp Corporation
キーワード
Attention mechanism
review classification
small corpus
transformer
資料タイプ
学術雑誌論文
抄録
This paper provides the classification of the review texts on a smartphone application posted on social media. We propose a high performance binary classification method (positive/negative) of review texts, which uses the bidirectional long short-term memory (biLSTM) self-attentional Transformer and is based on the distributed representations created by unsupervised learning of a manually labelled small review corpus, dictionary, and an unlabeled large review corpus. The proposed method obtained higher accuracy as compared to the existing methods, such as StarSpace or the Bidirectional Encoder Representations from Transformer (BERT).
掲載誌名
International Journal of Machine Learning and Computing
ISSN
20103700
10
1
開始ページ
148
終了ページ
157
発行日
2020-01
権利情報
© 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)(https://creativecommons.org/licenses/by/4.0/).
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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