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.





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