Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach

Abstract

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 <i>N</i>-gram methods (<i>N</i>-gram after, <i>N</i>-gram before, and <i>N</i>-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 <i>N</i>-gram around method with a precision of 81.93%, a recall of 81.13%, and an <i>F</i>-measure of 81.24%.