Sentiment Analysis on Tweets related to infectious diseases in South America

Infectious diseases have a huge social and economic impact. They are caused by pathogenic microorganisms such as bacteria, viruses, parasites or fungi and they can be transmitted, directly or indirectly, from one person to another or from animals to humans (Zoonoses). Nowadays it is very important to detect the infectious diseases as soon as possible to prevent critical problems for the society. In this work we propose an approach for the sentiment classification of tweets related to infectious diseases. This kind of systems could help health professionals to know how society respond to advances in the treatment of these diseases. In addition, a comparison was made of the performance of three classification algorithms (J48, BayesNet, and SMO). The results showed that SMO provides better results than BayesNet and J48 algorithms, obtaining an F-measure of 84.4%.