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. 2017:2017:5140631.
doi: 10.1155/2017/5140631. Epub 2017 Feb 19.

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

María Del Pilar Salas-Zárate  1 , José Medina-Moreira  2 , Katty Lagos-Ortiz  2 , Harry Luna-Aveiga  2 , Miguel Ángel Rodríguez-García  3 , Rafael Valencia-García  1
Affiliations

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

María Del Pilar Salas-Zárate et al. Comput Math Methods Med. 2017.

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 F-measure of 81.24%.

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Conflict of interest statement

The authors declare that there is no conflict of interests regarding the publication of this paper.

Figures

Figure 1
Figure 1
System architecture.
Figure 2
Figure 2
Tweet about diabetes.
Figure 3
Figure 3
Excerpt from DDO ontology.
Figure 4
Figure 4
Aspect-level sentiment analysis with N-gram methods.
See this image and copyright information in PMC

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