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

María del Pilar Salas-Zárate, José Medina-Moreira, Katty Lagos-Ortiz, Harry Luna-Aveiga, Miguel Angel Rodriguez-Garcia, Rafael Valencia-García

Research output: Contribution to journalArticlepeer-review

59 Scopus citations

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 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 -measure of 81.24%.
Original languageEnglish (US)
Pages (from-to)1-9
Number of pages9
JournalComputational and Mathematical Methods in Medicine
Volume2017
DOIs
StatePublished - Feb 19 2017

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