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A Timely Intervention: Tracking the Changing Meanings of Political Concepts with Word Vectors

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  • Rodman, Emma

Abstract

Word vectorization is an emerging text-as-data method that shows great promise for automating the analysis of semantics—here, the cultural meanings of words—in large volumes of text. Yet successes with this method have largely been confined to massive corpora where the meanings of words are presumed to be fixed. In political science applications, however, many corpora are comparatively small and many interesting questions hinge on the recognition that meaning changes over time. Together, these two facts raise vexing methodological challenges. Can word vectors trace the changing cultural meanings of words in typical small corpora use cases? I test four time-sensitive implementations of word vectors (word2vec) against a gold standard developed from a modest data set of 161 years of newspaper coverage. I find that one implementation method clearly outperforms the others in matching human assessments of how public dialogues around equality in America have changed over time. In addition, I suggest best practices for using word2vec to study small corpora for time series questions, including bootstrap resampling of documents and pretraining of vectors. I close by showing that word2vec allows granular analysis of the changing meaning of words, an advance over other common text-as-data methods for semantic research questions.

Suggested Citation

  • 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.
  • Handle: RePEc:cup:polals:v:28:y:2020:i:1:p:87-111_5
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    Cited by:

    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. 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.
    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. Andres Karjus & Christine Cuskley, 2024. "Evolving linguistic divergence on polarizing social media," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.

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