SentiHealth-Cancer: uma ferramenta de análise de sentimento para ajudar a detectar o humor de pacientes de câncer em uma rede social online

• Ano de defesa: 2016
• Autor(a) principal: Rodrigues, Ramon Gouveia
• Orientador(a): Camilo Junior, Celso Gonçalves
• Banca de defesa: Camilo Junior, Celso Gonçalves, Pappa, Gisele Lobo, Rosa, Thierson Couto
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: Análise de sentimento; Mineração de opinião; Câncer; Taxonomia
• Palavras-chave em Inglês: Sentiment analysis; Mining opinion; Cancer; Taxonomy
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/5910

Cancer is a critical disease that affects millions of people and families around the world. In 2012 about 14.1 million new cases of cancer occurred globally. Because of many reasons like the severity of some cases, the side effects of some treatments and death of other patients, cancer patients tend to be affected by serious emotional disorders, like depression. Thus, the use of a behavioral tool that assists the detection of the people mood can contribute to the monitoring of patients and family members during treatment. Therefore, the objective of this work is to develop a Sentiment Analysis tool, named SentiHealth-Cancer (SHC), to assist the detection of the emotional state of people members of Brazilian virtual communities for support cancer patients. We conducted a comparative study of the proposed method and a set of general-purpose Sentiment Analysis tools. For this, we collected 789 messages of 8 Facebook communities and considered 2.574 reviews of volunteers about the real sentiments expressed in these messages. Thus, the performance of the tools were tested in each community, with psychologists and non psychologists reviews and, where possible, with texts in Portuguese and translated into English. The results showed that, overall, the proposed method performance in this work is superior to other tools, both analyzing texts in Portuguese and English. For example, its accuracy (56.64%) analyzing all messages shows a significant increase of 11.78% compared to the greater accuracy (50.67%) presented by other tools.