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Knowing what you get when seeking semantic similarity: exploring classic NLP method biases

In: Handbook of Social Computing

Author

Listed:
  • Johanne Saint-Charles
  • Pierre Mongeau
  • Louis Renaud-Desjardins

Abstract

Various Natural Language Processing (NLP) methods are called upon to establish similarity between texts in the context of socio-semantic studies. This chapter addresses the methodological diversity in the field by asking to what extent classical NLP methods converge in their identification of similarity between various texts. We compare the results of well-known (and often used) NLP methods in social sciences and humanities: Jaccard, LDA, LSA and TF–IDF, on corpora with different characteristics. Results show that these methods have specific bias and cannot be substituted for one another. Our observations invite social sciences and humanities scholars to consider new criteria for the selection of an NLP method suited to their research objectives.

Suggested Citation

  • Johanne Saint-Charles & Pierre Mongeau & Louis Renaud-Desjardins, 2024. "Knowing what you get when seeking semantic similarity: exploring classic NLP method biases," Chapters, in: Peter A. Gloor & Francesca Grippa & Andrea Fronzetti Colladon & Aleksandra Przegalinska (ed.), Handbook of Social Computing, chapter 3, pages 27-46, Edward Elgar Publishing.
    • Handle: RePEc:elg:eechap:21469_3
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