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BERT- and BiLSTM-Based Sentiment Analysis of Online Chinese Buzzwords

Author

Listed:
  • Xinlu Li

    (Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China)

  • Yuanyuan Lei

    (Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China)

  • Shengwei Ji

    (Institute of Applied Optimization, School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China)

Abstract

Sentiment analysis of online Chinese buzzwords (OCBs) is important for healthy development of platforms, such as games and social networking, which can avoid transmission of negative emotions through prediction of users’ sentiment tendencies. Buzzwords have the characteristics of varying text length, irregular wording, ignoring syntactic and grammatical requirements, no complete semantic structure, and no obvious sentiment features. This results in interference and challenges to the sentiment analysis of such texts. Sentiment analysis also requires capturing effective sentiment features from deeper contextual information. To solve the above problems, we propose a deep learning model combining BERT and BiLSTM. The goal is to generate dynamic representations of OCB vectors in downstream tasks by fine-tuning the BERT model and to capture the rich information of the text at the embedding layer to solve the problem of static representations of word vectors. The generated word vectors are then transferred to the BiLSTM model for feature extraction to obtain the local and global semantic features of the text while highlighting the text sentiment polarity for sentiment classification. The experimental results show that the model works well in terms of the comprehensive evaluation index F1. Our model also has important significance and research value for sentiment analysis of irregular texts, such as OCBs.

Suggested Citation

  • Xinlu Li & Yuanyuan Lei & Shengwei Ji, 2022. "BERT- and BiLSTM-Based Sentiment Analysis of Online Chinese Buzzwords," Future Internet, MDPI, vol. 14(11), pages 1-15, November.
  • Handle: RePEc:gam:jftint:v:14:y:2022:i:11:p:332-:d:972376
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    References listed on IDEAS

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    1. Yousif A. Alhaj & Abdelghani Dahou & Mohammed A. A. Al-qaness & Laith Abualigah & Aaqif Afzaal Abbasi & Nasser Ahmed Obad Almaweri & Mohamed Abd Elaziz & Robertas Damaševičius, 2022. "A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language," Future Internet, MDPI, vol. 14(7), pages 1-18, June.
    2. Peng Ce & Bao Tie, 2020. "An Analysis Method for Interpretability of CNN Text Classification Model," Future Internet, MDPI, vol. 12(12), pages 1-14, December.
    3. Lun‐Wei Ku & Hsin‐Hsi Chen, 2007. "Mining opinions from the Web: Beyond relevance retrieval," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(12), pages 1838-1850, October.
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