IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2412.04505.html
   My bibliography  Save this paper

Achieving Semantic Consistency: Contextualized Word Representations for Political Text Analysis

Author

Listed:
  • Ruiyu Zhang
  • Lin Nie
  • Ce Zhao
  • Qingyang Chen

Abstract

Accurately interpreting words is vital in political science text analysis; some tasks require assuming semantic stability, while others aim to trace semantic shifts. Traditional static embeddings, like Word2Vec effectively capture long-term semantic changes but often lack stability in short-term contexts due to embedding fluctuations caused by unbalanced training data. BERT, which features transformer-based architecture and contextual embeddings, offers greater semantic consistency, making it suitable for analyses in which stability is crucial. This study compares Word2Vec and BERT using 20 years of People's Daily articles to evaluate their performance in semantic representations across different timeframes. The results indicate that BERT outperforms Word2Vec in maintaining semantic stability and still recognizes subtle semantic variations. These findings support BERT's use in text analysis tasks that require stability, where semantic changes are not assumed, offering a more reliable foundation than static alternatives.

Suggested Citation

  • Ruiyu Zhang & Lin Nie & Ce Zhao & Qingyang Chen, 2024. "Achieving Semantic Consistency: Contextualized Word Representations for Political Text Analysis," Papers 2412.04505, arXiv.org, revised Jan 2025.
    • Handle: RePEc:arx:papers:2412.04505
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2412.04505
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rodman, Emma, 2020. "A Timely Intervention: Tracking the Changing Meanings of Political Concepts with Word Vectors," Political Analysis, Cambridge University Press, vol. 28(1), pages 87-111, January.
    Full references (including those not matched with items on IDEAS)

    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. Stijn Daenekindt & Julian Schaap, 2022. "Using word embedding models to capture changing media discourses: a study on the role of legitimacy, gender and genre in 24,000 music reviews, 1999–2021," Journal of Computational Social Science, Springer, vol. 5(2), pages 1615-1636, November.
    2. Andres Karjus & Christine Cuskley, 2024. "Evolving linguistic divergence on polarizing social media," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    3. Gloria Gennaro & Elliott Ash, 2022. "Emotion and Reason in Political Language," The Economic Journal, Royal Economic Society, vol. 132(643), pages 1037-1059.
    4. Miguel Won & Jorge M. Fernandes, 2022. "Analyzing Twitter networks using graph embeddings: an application to the British case," Journal of Computational Social Science, Springer, vol. 5(1), pages 253-263, May.

    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:arx:papers:2412.04505. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.