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Microblog Sentiment Analysis Based on Dynamic Character-Level and Word-Level Features and Multi-Head Self-Attention Pooling

Author

Listed:
  • Shangyi Yan

    (College of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China)

  • Jingya Wang

    (College of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China)

  • Zhiqiang Song

    (College of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China)

Abstract

To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve the effectiveness of microblog sentiment analysis. The model employs WOBERT Plus and ALBERT to dynamically encode character-level text and word-level text, respectively. Then, a convolution operation is used to extract local key features, while cross-channel feature fusion and multi-head self-attention pooling operations are used to extract global semantic information and filter out key data, before using the multi-granularity feature interaction fusion operation to effectively fuse character-level and word-level semantic information. Finally, the Softmax function is used to output the results. On the weibo_senti_100k dataset, the accuracy and F1 values of the DCCMM model improve by 0.84% and 1.01%, respectively, compared to the best-performing comparison model. On the SMP2020-EWECT dataset, the accuracy and F1 values of the DCCMM model improve by 1.22% and 1.80%, respectively, compared with the experimental results of the best-performing comparison model. The results showed that DCCMM outperforms existing advanced sentiment analysis models.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:8:p:234-:d:875403
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    References listed on IDEAS

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    1. Pin Yang & Huiyu Zhou & Yue Zhu & Liang Liu & Lei Zhang, 2020. "Malware Classification Based on Shallow Neural Network," Future Internet, MDPI, vol. 12(12), pages 1-17, December.
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    3. Najla M. Alharbi & Norah S. Alghamdi & Eman H. Alkhammash & Jehad F. Al Amri, 2021. "Evaluation of Sentiment Analysis via Word Embedding and RNN Variants for Amazon Online Reviews," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-10, May.
    4. Yong Fang & Jian Gao & Cheng Huang & Hua Peng & Runpu Wu, 2019. "Self Multi-Head Attention-based Convolutional Neural Networks for fake news detection," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-13, September.
    5. Areej Alsini & Du Q. Huynh & Amitava Datta, 2021. "Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review," Future Internet, MDPI, vol. 13(5), pages 1-19, May.
    6. Chenyuan Hu & Shuoyan Zhang & Tianyu Gu & Zhuangzhi Yan & Jiehui Jiang, 2022. "Multi-Task Joint Learning Model for Chinese Word Segmentation and Syndrome Differentiation in Traditional Chinese Medicine," IJERPH, MDPI, vol. 19(9), pages 1-13, May.
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    Cited by:

    1. Jinlong Wang & Dong Cui & Qiang Zhang, 2023. "Chinese Short-Text Sentiment Prediction: A Study of Progressive Prediction Techniques and Attentional Fine-Tuning," Future Internet, MDPI, vol. 15(5), pages 1-20, April.
    2. 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.

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