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IJMLC 2020 Vol.10(1): 148-157 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.1.912

Classification of Smartphone Application Reviews Using Small Corpus Based on Bidirectional LSTM Transformer

Kazuyuki Matsumoto, Seiji Tsuchiya, Takumi Kojima, Hiroya Kondo, Minoru Yoshida, and Kenji Kita

Abstract—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).

Index Terms—Attention mechanism, review classification, small corpus, transformer.

K. Matsumoto, T. Kojima, M. Yoshida, K. Kita are with Tokushima University, Minamijosanjima-cho 2-1, 7708506, Japan (e-mail: matumoto@ is.tokushima-u.ac.jp, mino@ is.tokushima-u.ac.jp, kita@ is.tokushima-u.ac.jp).
S. Tsuchiya is with Doshisha University, Kyoto, Japan (e-mail: stsuchiy@mail.doshisha.ac.jp).
H. Kondo is with Sharp Corporation, 5908522, Osakafu, Sakai-shi, Takumicho-1, Japan.

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Cite: Kazuyuki Matsumoto, Seiji Tsuchiya, Takumi Kojima, Hiroya Kondo, Minoru Yoshida, and Kenji Kita, "Classification of Smartphone Application Reviews Using Small Corpus Based on Bidirectional LSTM Transformer," International Journal of Machine Learning and Computing vol. 10, no. 1, pp. 148-157, 2020.

Copyright © 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).

 

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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