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.

语言是社会问题和价值体系的反映。本研究试图通过语言的使用来了解塞内加尔的敏感公共问题。为此，我们利用词嵌入（一种数字词表示）来分析几个词的概念、内涵和细微差别。最先进的机器学习方法可以有效地从文本集合中提取词嵌入。由于不同社会的人们有不同的思维方式和语言使用，比较不同语料库中单词的语义差异是得出跨文化见解和含义的有效方法。在这项研究中，我们从塞内加尔报纸和法语维基百科页面中提取词嵌入，然后比较结果以识别塞内加尔文化中的不同词情感，以了解该国的过去、现在和未来。