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ID 114311
Author
Tsuchiya, Seiji Doshisha University
Kojima, Takumi Tokushima University
Kondo, Hiroya Sharp Corporation
Keywords
Attention mechanism
review classification
small corpus
transformer
Content Type
Journal Article
Description
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).
Journal Title
International Journal of Machine Learning and Computing
ISSN
20103700
Volume
10
Issue
1
Start Page
148
End Page
157
Published Date
2020-01
Rights
© 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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DOI (Published Version)
URL ( Publisher's Version )
FullText File
language
eng
TextVersion
Publisher
departments
Science and Technology