Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach.
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 throughN-gram methods (N-gram after,N-gram before, andN-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 theN-gram around method with a precision of 81.93%, a recall of 81.13%, and anF-measure of 81.24%.
Departamento de Informática y Sistemas, Universidad de Murcia, 30100 Murcia, Spain.
María Del Pilar
0000-0003-1818-3434
MD
Salas-Zárate
Universidad de Guayaquil, Cdla. Universitaria Salvador Allende, Guayaquil, Ecuador.
José
J
Medina-Moreira
Universidad de Guayaquil, Cdla. Universitaria Salvador Allende, Guayaquil, Ecuador.
Katty
0000-0002-2510-7416
K
Lagos-Ortiz
Universidad de Guayaquil, Cdla. Universitaria Salvador Allende, Guayaquil, Ecuador.
Harry
H
Luna-Aveiga
Computational Bioscience Research Center, King Abdullah University of Science and Technology, 4700 KAUST, P.O. Box 2882, Thuwal 23955-6900, Saudi Arabia.
Miguel Ángel
0000-0001-6244-6532
MÁ
Rodríguez-García
Departamento de Informática y Sistemas, Universidad de Murcia, 30100 Murcia, Spain.
Rafael
0000-0003-2457-1791
R
Valencia-García
D000465:Algorithms; D001290:Attitude; D016208:Databases, Factual; D003920:Diabetes Mellitus / Q000175:diagnosis* / Q000628:therapy*; D004644:Emotions; D006801:Humans; D020407:Internet; D007802:Language; D008037:Linguistics; D008490:Medical Informatics; D015233:Models, Statistical; D010353:Patient Education as Topic / Q000379:methods*; D010379:Peer Group; D015203:Reproducibility of Results; D012660:Semantics; D061108:Social Media; D012944:Social Support; D060388:Support Vector Machine