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%.

The use of ontologies for representing knowledge provides us with organization, communication and reusability. Information security is a serious requirement which must be carefully considered. Concepts and relations managed by any scientific community need to be formally defined and ontological engineering supports their definition. In this paper, the method of systematic review is applied with the purpose of identifying, extracting and analyzing the main proposals for security ontologies. The main identified proposals are compared using a formal framework and we conclude by stating their early state of development and the need of additional research efforts.