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A Multi-filter BiLSTM-CNN Architecture for Vietnamese Sentiment Analysis

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Advances in Computational Collective Intelligence (ICCCI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1287))

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Abstract

Feedback is information about reactions to a product or a person’s performance of a task. It is powerful as it serves as a guide to assist people to know how others perceive their performance and helps them meet standards. This paper concentrates on the use of natural language processing and deep learning. It combines the advantages of these approaches to perform sentiment analysis on student and customer feedback. Furthermore, word embedding is also applied to the model to add complementary effectiveness. The preliminary findings show that the use of BiLSTM-CNN–the first to catch the temporary information of the data and the second to extract the local structure thereof–outperformed other algorithms in terms of the F1-score measurement, with 93.55% for the Vietnamese Student’s Feedback Corpus (VSFC) and 84.14% for the Vietnamese Sentiment (VS). The results demonstrate that our method is an improvement compared to the best previously proposed methods on the two datasets.

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Notes

  1. 1.

    Vietnamese-stopwords: https://github.com/stopwords/vietnamese-stopwords.

  2. 2.

    PyVi: https://pypi.org/project/pyvi/.

  3. 3.

    RAdam: https://github.com/LiyuanLucasLiu/RAdam.

  4. 4.

    Gensim: https://radimrehurek.com/gensim/models/word2ve.

  5. 5.

    DeepLearning4j: https://deeplearning4j.org.

  6. 6.

    Gensim: https://radimrehurek.com/gensim/.

  7. 7.

    The Stanford Classifier: https://nlp.stanford.edu/software/classifier.shtml.

  8. 8.

    A term for Vietnamese abbreviations of young people in Vietnam. Even each young group has its own creative way to differentiate itself from the rest.

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Acknowledgments

Our thanks especially go to the authors of VSFC and VS for providing Vietnamese datasets; these provide us with invaluable data for experiments with our models. This research is funded by the University of Information Technology - Vietnam National University Ho Chi Minh City under grant number B2019-26-01.

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Authors and Affiliations

  1. University of Information Technology, Ho Chi Minh City, Vietnam

    Lac Si Le, Dang Van Thin, Ngan Luu-Thuy Nguyen & Son Quoc Trinh

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Lac Si Le, Dang Van Thin, Ngan Luu-Thuy Nguyen & Son Quoc Trinh

Authors
  1. Lac Si Le
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  2. Dang Van Thin
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  3. Ngan Luu-Thuy Nguyen
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  4. Son Quoc Trinh
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Corresponding author

Correspondence to Dang Van Thin .

Editor information

Editors and Affiliations

  1. Wroclaw University of Economics and Business, Wrocław, Poland

    Marcin Hernes

  2. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  3. University of Newcastle, Newcastle, Australia

    Edward Szczerbicki

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Le, L.S., Thin, D.V., Nguyen, N.LT., Trinh, S.Q. (2020). A Multi-filter BiLSTM-CNN Architecture for Vietnamese Sentiment Analysis. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_61

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  • DOI: https://doi.org/10.1007/978-3-030-63119-2_61

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