Sentiment Analysis of Twitter’s Health Messages in Brazilian Portuguese

Gabriela Denise de Araujo, Fabio Oliveira Teixeira, Felipe Mancini, Marcelo de Paiva Guimarães, Ivan Torres Pisa


Objective: To present results of a sentiment classification methodology, here denominated Sentiment Descriptor Indexing (SDI), to be applied in Brazilian Portuguese Twitter’s messages related to health topics. Methods: The first step considered the construction of an algorithm that is based on the co-occurrence of Twitter terms with sentiment descriptor vocabulary known as ANEW-BR. In the second stage, an evaluation of SDI algorithm performance for messages about “cancer” of a period of three weeks was performed. The ratings were paired, to generate a performance appraisal.Results: The precision and recall values   were 0.68 and 0.67, respectively. A total of 25,230 messages on the topic “cancer” with a positive feeling classification (71%) were collected. Conclusion: The contributions of this work aim to fill the lack of methods of analysis of feelings for the Portuguese Portuguese language.


Social Media; Cancer; Delivery of Health Care

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Journal of Health Informatics - ISSN 2175-4411
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