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Parsimonious Wasserstein Text-mining

Author

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  • Gadat, Sébastien
  • Villeneuve, Stéphane

Abstract

This document introduces a parsimonious novel method of processing textual data based on the NMF factorization and on supervised clustering withWasserstein barycenter’s to reduce the dimension of the model. This dual treatment of textual data allows for a representation of a text as a probability distribution on the space of profiles which accounts for both uncertainty and semantic interpretability with the Wasserstein distance. The full textual information of a given period is represented as a random probability measure. This opens the door to a statistical inference method that seeks to predict a financial data using the information generated by the texts of a given period.

Suggested Citation

  • Gadat, Sébastien & Villeneuve, Stéphane, 2023. "Parsimonious Wasserstein Text-mining," TSE Working Papers 23-1471, Toulouse School of Economics (TSE).
    • Handle: RePEc:tse:wpaper:128497
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    References listed on IDEAS

    as
    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. repec:hal:spmain:info:hdl:2441/1293p84sf58s482v2dpn0gsd67 is not listed on IDEAS
    3. Alfred Galichon & Bernard Salanié, 2010. "Matching with Trade-offs: Revealed Preferences over Competiting Characteristics," Working Papers hal-00473173, HAL.
    4. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    5. Kearney, Colm & Liu, Sha, 2014. "Textual sentiment in finance: A survey of methods and models," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 171-185.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Natural Language Processing; Textual Analysis; Wasserstein distance; clustering;
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