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Sentiment Analysis of Chinese Product Reviews Based on Fusion of DUAL-Channel BiLSTM and Self-Attention

Author

Listed:
  • Ye Yuan

    (College of Engineering, Shantou University, Shantou 515063, China)

  • Wang Wang

    (College of Engineering, Shantou University, Shantou 515063, China)

  • Guangze Wen

    (College of Engineering, Shantou University, Shantou 515063, China)

  • Zikun Zheng

    (College of Engineering, Shantou University, Shantou 515063, China)

  • Zhemin Zhuang

    (College of Engineering, Shantou University, Shantou 515063, China)

Abstract

Product reviews provide crucial information for both consumers and businesses, offering insights needed before purchasing a product or service. However, existing sentiment analysis methods, especially for Chinese language, struggle to effectively capture contextual information due to the complex semantics, multiple sentiment polarities, and long-term dependencies between words. In this paper, we propose a sentiment classification method based on the BiLSTM algorithm to address these challenges in natural language processing. Self-Attention-CNN BiLSTM (SAC-BiLSTM) leverages dual channels to extract features from both character-level embeddings and word-level embeddings. It combines BiLSTM and Self-Attention mechanisms for feature extraction and weight allocation, aiming to overcome the limitations in mining contextual information. Experiments were conducted on the onlineshopping10cats dataset, which is a standard corpus of e-commerce shopping reviews available in the ChineseNlpCorpus 2018. The experimental results demonstrate the effectiveness of our proposed algorithm, with Recall, Precision, and F1 scores reaching 0.9409, 0.9369, and 0.9404, respectively.

Suggested Citation

  • Ye Yuan & Wang Wang & Guangze Wen & Zikun Zheng & Zhemin Zhuang, 2023. "Sentiment Analysis of Chinese Product Reviews Based on Fusion of DUAL-Channel BiLSTM and Self-Attention," Future Internet, MDPI, vol. 15(11), pages 1-19, November.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:11:p:364-:d:1277591
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    References listed on IDEAS

    as
    1. Shangyi Yan & Jingya Wang & Zhiqiang Song, 2022. "Microblog Sentiment Analysis Based on Dynamic Character-Level and Word-Level Features and Multi-Head Self-Attention Pooling," Future Internet, MDPI, vol. 14(8), pages 1-19, July.
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