Análise de sentimento e desambiguação no contexto da tv social

• Ano de defesa: 2012
• Autor(a) principal: Lima, Ana Carolina Espírito Santo
• Orientador(a): Silva, Leandro Nunes de Castro
• Banca de defesa: Mustaro, Pollyana Notargiacomo , Prati, Ronaldo Cristiano
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Presbiteriana Mackenzie
• Programa de Pós-Graduação: Engenharia Elétrica
• Departamento: Engenharia Elétrica
• País: BR
• Palavras-chave em Português: mineração de textos; análise de sentimento; desambiguação de sentido; mídias sociais; twitter; aprendizagem de máquina
• Palavras-chave em Inglês: text mining; sentiment analysis; word sense disambiguation; social media; twitter; machine learning
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://dspace.mackenzie.br/handle/10899/24341

Social media have become a way of expressing collective interests. People are motivated by the sharing of information and the feedback from friends and colleagues. Among the many social media tools available, the Twitter microblog is gaining popularity as a platform for in-stantaneous communication. Millions of messages are generated daily, from over 100 million users, about the most varied subjects. As it is a rapid communication platform, this microblog spurred a phenomenon called television storytellers, where surfers comment on what they watch on TV while the programs are being transmitted. The Social TV emerged from this integration between social media and television. The amount of data generated on the TV shows is a rich material for data analysis. Broadcasters may use such information to improve their programs and increase interaction with their audience. Among the main challenges in social media data analysis there is sentiment analysis (to determine the polarity of a text, for instance, positive or negative), and sense disambiguation (to determine the right context of polysemic words). This dissertation aims to use machine learning techniques to create a tool to support Social TV, contributing specifically to the automation of sentiment analysis and disambiguation of Twitter messages.