Identificação e propagação de temas em redes sociais

• Ano de defesa: 2016
• Autor(a) principal: Klinczak, Marjori Naiele Mocelin
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Brasil; Programa de Pós-Graduação em Computação Aplicada; UTFPR
• Palavras-chave em Português: Mineração de dados (Computação); Mineração de uso da Web; Redes sociais on-line; Computação; Data mining; Web usage mining; Online social networks; Computer science; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO; Ciência da Computação
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/2304

Recent years have been marked by the emergence of various social media, from Orkut to Facebook, and Twitter, Youtube, Google+ and many others: each offers new features as a way to attract more users. These social media generate a large amount of data which is processed properly can be used to identify trends, patterns and changes. The objective of this work is the discovery of the key topics in a social network, characterized as relevant terms groupings, restricted to a particular context and the study of its evolution over time. For that will be used procedures based on Data Mining and Text Processing. At first techniques are used preprocessing of texts in order to identify the most relevant terms that appear in the text messages from the social network. Next are used grouping of classical algorithms - k-means, k-medoids, DBSCAN - and the recent NMF (Non-negative Matrix Factorization), to identify the main themes of these messages, characterized as relevant terms groupings. The proposal was evaluated on the Twitter network, using bases tweets considering different contexts. The results show the feasibility of the proposal and its application in the identification of relevant topics of this social network