Salas-Zárate María Del Pilar, Medina-Moreira José, Lagos-Ortiz Katty, Luna-Aveiga Harry, Rodríguez-García Miguel Ángel, Valencia-García Rafael
Departamento de Informática y Sistemas, Universidad de Murcia, 30100 Murcia, Spain.
Universidad de Guayaquil, Cdla. Universitaria Salvador Allende, Guayaquil, Ecuador.
Comput Math Methods Med. 2017;2017:5140631. doi: 10.1155/2017/5140631. Epub 2017 Feb 19.
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 -gram methods (-gram after, -gram before, and -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 -gram around method with a precision of 81.93%, a recall of 81.13%, and an -measure of 81.24%.
近年来，已经为健康领域开发了一些情感分析方法；然而，糖尿病领域尚未得到探索。此外，缺乏分析文档（如综述、新闻和推文等）中包含的各个方面的积极或消极倾向的方法。基于这种认识，我们提出了一种基于本体的糖尿病领域方面级情感分析方法。通过考虑通过 -gram 方法（后 -gram、前 -gram 和周围 -gram）获得的方面周围的单词来计算各方面的情感。为了评估我们方法的有效性，我们从 Twitter 获得了一个语料库，该语料库已在方面级别手动标记为积极、消极或中性。实验结果表明，通过周围 -gram 方法获得了最佳结果，精确率为81.93%，召回率为81.13%，F值为81.24%。
