Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach
AuthorsSalas-Zárate, María del Pilar
Rodriguez-Garcia, Miguel Angel
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2017-02-19
Print Publication Date2017
Permanent link to this recordhttp://hdl.handle.net/10754/622969
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AbstractIn 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%.
CitationSalas-Zárate M del P, Medina-Moreira J, Lagos-Ortiz K, Luna-Aveiga H, Rodríguez-García MÁ, et al. (2017) Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach. Computational and Mathematical Methods in Medicine 2017: 1–9. Available: http://dx.doi.org/10.1155/2017/5140631.
SponsorsThis work has been funded by the Universidad de Guayaquil (Ecuador) through the project entitled “Tecnologías Inteligentes para la Autogestión de la Salud.” María del Pilar Salas-Zárate is supported by the National Council of Science and Technology (CONACYT), the Public Education Secretary (SEP), and the Mexican Government. Finally, this work has been also partially supported by the Spanish Ministry of Economy and Competitiveness and the European Commission (FEDER/ERDF) through project KBS4FIA (TIN2016-76323-R).
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