IDEAS home Printed from https://ideas.repec.org/a/hin/jnddns/9879986.html
   My bibliography  Save this article

Sentiment Analysis of International and Foreign Chinese-Language Texts with Multilevel Features

Author

Listed:
  • Minxia Zhu
  • Zaoli Yang

Abstract

This paper takes the application of international Chinese to foreigners on the Internet as the research object. A variety of features are constructed according to the characteristics of international and foreign Chinese texts and networks. This paper selects three features: dictionary-based sentiment value feature, expression feature, and improved semantic feature. A text sentiment classification model is formed by fusing multiple features. Compared with the traditional model and other single-feature models on the self-built dataset, the experimental results show that its sentiment classification ability has been effectively improved. The results show that the accuracy, recall, and F1 value of the fused multilevel feature MFCNN model are much higher than the accuracy, recall, and F1 value of other models. This also shows that the improved model of this method has a better effect of improving the accuracy.

Suggested Citation

  • Minxia Zhu & Zaoli Yang, 2022. "Sentiment Analysis of International and Foreign Chinese-Language Texts with Multilevel Features," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-12, May.
    • Handle: RePEc:hin:jnddns:9879986
    DOI: 10.1155/2022/9879986
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/ddns/2022/9879986.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/ddns/2022/9879986.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/9879986?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnddns:9879986. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.