Abstract
Understanding the sentiment nature of Trade Policy
documents is a crucial step in both fostering global and regional
trade. Moreso, the sentiments present in trade policy documents
offer valuable insights into the direction, attitudes, and political
atmosphere amongst member countries. This research aims to
provide a sentiment analysis of one of Africa’s crucial trading
blocs, COMESA, using Natural Language Processing. By using
Machine Learning techniques, we arrived at the conclusion
that the COMESA Trade Policy document has mostly positive
sentiments with preferences for ’Member States’, ’Transit of
Goods’ and ’Common Market’ to name a few. The research
will lay the groundwork for future sentiment analysis of other
global and regional trading blocs.