Semantic and Morpho-Syntactic Prevention Guidelines for COVID-19 Based on Cognitively Inspired Artificial Intelligence and Data Mining. Case Study: Europe, North America, and South America (2021)
Based on a combination of cognitively inspired methods in artificial intelligence such as artificial mathematical intelligence and data mining, we study the correlation between the COVID-19 pandemic and the sentiment analysis (qualitative ontological nature) of tweets and their linguistic patterns from the presidents and the populations of five countries from Europe (Spain and the United Kingdom), North America (The United States of America), and South America (Chile and Colombia). The results show that tweets classified as negative are the most common in all presidential tweeter accounts, except in one country, Colombia. However, tweets classified as neutral are dominant in the population tweets in each country examined. Based on the results obtained and on some of the foundational cognitive techniques of artificial mathematical intelligence, we conclude by providing COVID-19 prevention guidelines at the linguistic and cognitive levels.