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

Kamwoo Lee (), Jeanine Braithwaite and Michel Atchikpa
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Kamwoo Lee: University of Virginia
Jeanine Braithwaite: University of Virginia
Michel Atchikpa: Université Cheikh Anta Diop de Dakar

Computational and Mathematical Organization Theory, 2021, vol. 27, issue 3, No 5, 343-356

Abstract: 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.

Keywords: Senegal; Colonial legacy; Environmental issues; Optimism measurement; Word embeddings. (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10588-021-09335-y

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