Skip to main content
SpringerLink
Log in

Word embedding analysis on colonial history, present issues, and optimism toward the future in Senegal

  • S.I. : SBP-BRiMS2020
  • Published:
Computational and Mathematical Organization Theory Aims and scope Submit manuscript

Abstract

Language is a reflection of issues and value systems of a society. This study tries to understand sensitive public issues in Senegal through language use. To this end, we utilize word embeddings, a numerical word representation, to analyze concepts, connotations, and nuances of several words. State-of-the-art machine learning methods can effectively extract the word embeddings from a collection of texts. Since people in different societies possess different mindsets and language uses, comparing semantic differences of words in different corpora is an efficient way to draw cross-cultural insights and implications. In this study, we extract word embeddings from Senegalese newspapers and Wikipedia pages in French and then compare the results to identify different word sentiments in Senegalese cultures to understand the past, present, and future of the country.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. actusen.sn, www.dakaractu.com, www.dakarmatin.com, www.dakarmidi.net, www.ferloo.com, homeviewsenegal.com, www.impact.sn, www.koldanews.com, www.lequotidien.sn, www.leral.net, letemoin.sn, senego.com, www.sudonline.sn, www.teranganews.sn.

References

  • African Development Bank (2012) Projet d’assainissement de la ville de dakar

  • Attardi G (2009) Wikiextractor: A tool for extracting plain text from wikipedia dumps

  • Bolukbasi T, Chang KW, Zou JY, Saligrama V, Kalai AT (2016) Man is to computer programmer as woman is to homemaker? debiasing word embeddings. In: Advances in neural information processing systems, pp 4349–4357

  • Bruni E, Boleda G, Baroni M, Tran NK (2012) Distributional semantics in technicolor. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 136–145

  • Bruni E, Tran NK, Baroni M (2014) Multimodal distributional semantics. J Artif Intell Res 49:1–47

    Article  Google Scholar 

  • Caliskan A, Bryson JJ, Narayanan A (2017) Semantics derived automatically from language corpora contain human-like biases. Science 356(6334):183–186

    Article  Google Scholar 

  • Diallo I (2011) ‘To understand lessons, think through your own languages.’ An analysis of narratives in support of the introduction of indigenous languages in the education system in senegal. Lang Matters 42(2):207–230

  • Diouf M (1998) The french colonial policy of assimilation and the civility of the originaires of the four communes (senegal): A nineteenth century globalization project. Dev Chang 29(4):671–696

    Article  Google Scholar 

  • Diouf M (2013) Tolerance, democracy, and Sufis in Senegal. Columbia University Press, Columbia

    Book  Google Scholar 

  • Garcia L (2015) Impact du changement climatique sur les rendements du mil et de l’arachide au sénégal: Approche par expérimentation virtuelle. PhD thesis, Montpellier SupAgro

  • Graham C (2012) Happiness around the world: the paradox of happy peasants and miserable millionaires. Oxford University Press, Oxford

  • Grave E, Bojanowski P, Gupta P, Joulin A, Mikolov T (2018) Learning word vectors for 157 languages. arXiv preprint arXiv:180206893

  • Hamilton WL, Leskovec J, Jurafsky D (2016) Diachronic word embeddings reveal statistical laws of semantic change. arXiv preprint arXiv:160509096

  • Kozlowski AC, Taddy M, Evans JA (2019) The geometry of culture: analyzing the meanings of class through word embeddings. Am Sociol Rev 84(5):905–949

    Article  Google Scholar 

  • Lewis MD (1962) One hundred million frenchmen: the “assimilation” theory in french colonial policy. Compar Stud Soci Hist 4(2):129–153

  • Maaten Lvd, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(Nov):2579–2605

  • Pennington J, Socher R, Manning C (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

  • Rawley JA, Behrendt SD (2005) The transatlantic slave trade: a history. University of Nebraska Press, Lincoln

  • Řehůřek R, Sojka P (2010) Software Framework for Topic Modelling with Large Corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, ELRA, Valletta, Malta, pp 45–50, http://is.muni.cz/publication/884893/en

  • Rosling H, Rönnlund A, Rosling O (2018) Factfulness: ten reasons we’re wrong about the world–and why things are better than you think. Flatiron Books, https://books.google.com/books?id=j-4yDwAAQBAJ

  • Salhi K (2004) Rethinking francophone culture: Africa and the caribbean between history and theory. Res Afr Lit 35(1):9–29

    Google Scholar 

  • Sène A, Sarr MA, Kane A, Diallo M (2018) L’assèchement des lacs littoraux de la grande côte du sénégal: mythe ou réalité? cas des lacs thiourour warouwaye et wouye de la banlieue de dakar. J Anim Plant Sci 35(2):5623–5638

    Google Scholar 

  • Turian J, Ratinov L, Bengio Y (2010) Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th annual meeting of the association for computational linguistics. Association for Computational Linguistics, pp 384–394

  • World Bank Group (2018) Systematic country diagnostic of senegal. World Bank, Dakar. http://hdl.handle.net/10986/30852

  • World Bank Group (2019) Senegal. https://www.worldbank.org/en/country/senegal/overview. Accessed 6 Oct 2019

  • Yao Z, Sun Y, Ding W, Rao N, Xiong H (2018) Dynamic word embeddings for evolving semantic discovery. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, ACM, pp 673–681

Download references

Acknowledgements

This research was supported by the Global Infectious Disease Institute at the University of Virginia (UVA). We also thank Senegal Research Group members at UVA for their feedback to this research: Grace Wood, Gabrielle Posner, and Jordan Beeker. We also would like to show our deepest gratitude to Fama Gueye, an exchange scholar from Université Cheikh Anta Diop de Dakar for her insights in social issues of Senegal.

Author information

Authors and Affiliations

  1. Systems and Information Engineering, University of Virginia, Charlottesville, USA

    Kamwoo Lee

  2. Frank Batten School of Leadership and Public Policy, University of Virginia, Charlottesville, USA

    Jeanine Braithwaite

  3. Centre de Recherches Économiques Appliquées, Université Cheikh Anta Diop de Dakar, Dakar, Senegal

    Michel Atchikpa

Authors
  1. Kamwoo Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeanine Braithwaite
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michel Atchikpa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kamwoo Lee.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, K., Braithwaite, J. & Atchikpa, M. Word embedding analysis on colonial history, present issues, and optimism toward the future in Senegal. Comput Math Organ Theory 27, 343–356 (2021). https://doi.org/10.1007/s10588-021-09335-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10588-021-09335-y

Keywords

Search

Navigation