IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i8p234-d875403.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/8/234/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/14/8/234/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Mingyun Gu & Haixiang Guo & Jun Zhuang & Yufei Du & Lijin Qian, 2022. "Social Media User Behavior and Emotions during Crisis Events," IJERPH, MDPI, vol. 19(9), pages 1-21, April.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Md Doulotuzzaman Xames & Fariha Kabir Torsha & Ferdous Sarwar, 2023. "A systematic literature review on recent trends of machine learning applications in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2529-2555, August.
    2. Hongzhou Shen & Yue Ju & Zhijing Zhu, 2023. "Extracting Useful Emergency Information from Social Media: A Method Integrating Machine Learning and Rule-Based Classification," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    3. Senqi Yang & Xuliang Duan & Zeyan Xiao & Zhiyao Li & Yuhai Liu & Zhihao Jie & Dezhao Tang & Hui Du, 2022. "Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN," IJERPH, MDPI, vol. 19(20), pages 1-20, October.
    4. Wongchai, Anupong & Jenjeti, Durga rao & Priyadarsini, A. Indira & Deb, Nabamita & Bhardwaj, Arpit & Tomar, Pradeep, 2022. "Farm monitoring and disease prediction by classification based on deep learning architectures in sustainable agriculture," Ecological Modelling, Elsevier, vol. 474(C).
    5. Andreea-Maria Copaceanu, 2021. "Sentiment Analysis Using Machine Learning Approach," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 261-270, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:14:y:2022:i:8:p:234-:d:875403. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.