In recent years, some methods of sentiment analysis have been developed for the health domain; however, the diabetes domain has not been explored yet. In addition, there is a lack of approaches that analyze the positive or negative orientation of each aspect contained in a document (a review, a piece of news, and a tweet, among others). Based on this understanding, we propose an aspect-level sentiment analysis method based on ontologies in the diabetes domain. The sentiment of the aspects is calculated by considering the words around the aspect which are obtained through N-gram methods (N-gram after, N-gram before, and N-gram around). To evaluate the effectiveness of our method, we obtained a corpus from Twitter, which has been manually labelled at aspect level as positive, negative, or neutral. The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an F-measure of 81.24%.

In recent years, a substantial effort has been made to develop sophisticated methods that can be used to detect figurative language, and more specifically, irony and sarcasm. There is, however, an absence of new approaches and research works that analyze satirical texts. The recognition of satire by sentiment analysis and Natural Language Processing (NLP) applications is extremely important because it can influence and change the meaning of a statement in varied and complex ways. We used this understanding as a basis to propose a method that employs a wide variety of psycholinguistic features and which detects satirical and non-satirical text. We then went on to train a set of machine learning algorithms that would enable us to classify unknown data. Finally, we conducted several experiments in order to detect the most relevant features that generate a better pattern as regards detecting satirical texts. We evaluated the effectiveness of our method by obtaining a corpus of satirical and non-satirical news from Mexican and Spanish twitter accounts. Our proposal obtained encouraging results, with an F-measure of 85.5% for Mexico and one of 84.0% for Spain. Moreover, the results of the experiment showed that there is no significant difference between Mexican and Spanish satire

Obesity is considered an epidemic that is continuously growing around the world. Heart diseases, diabetes, and bone and joint diseases are some of the diseases that people who are overweight or obese can develop. One of the vital causes of those disorders is poor nutrition education; there is no raising awareness about eating healthy food and practicing physical activities to burn off the excess energy. Therefore, it is necessary to use new technologies to build methods/tools that help people to overcome these avoidable nutrition disorders. For this reason, we implemented a recommendation engine capable of identifying the different levels of overweight and obesity in users and providing dietary strategies to mitigate them. To do so, we defined the Ontology of Dietary Recommendations (ODR) with axioms to model recipes, ingredients, and a set of diets to assist people who suffer from obesity. We validated the defined model by using a real set of individuals who were anonymized. A panel of advisors evaluated each individual record and suggested the most appropriate diets from those included in the ontology. Then, the proposed system was asked to also provide diet recommendations for each individual, which were compared with those proposed by the advisors (ground truth), reaching a mean accuracy of 87%.