T
Tulio C. Alberto
Researcher at Federal University of São Carlos
Publications - 6
Citations - 147
Tulio C. Alberto is an academic researcher from Federal University of São Carlos. The author has contributed to research in topics: Text normalization & Normalization (statistics). The author has an hindex of 4, co-authored 6 publications receiving 87 citations.
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Proceedings ArticleDOI
TubeSpam: Comment Spam Filtering on YouTube
TL;DR: The statistical analysis of results indicate that, with 99.9% of confidence level, decision trees, logistic regression, Bernoulli Naive Bayes, random forests, linear and Gaussian SVMs are statistically equivalent for comment spam filtering on YouTube.
Journal ArticleDOI
Towards filtering undesired short text messages using an online learning approach with semantic indexing
TL;DR: A new hybrid ensemble approach is proposed that combines the predictions obtained by the classifiers using the original text samples along with their variations created by applying text normalization and semantic indexing techniques, which can improve the text content quality and enhance the performance of the expert systems for spamming detection.
Journal ArticleDOI
Post or Block? Advances in Automatically Filtering Undesired Comments
TL;DR: Experiments carried out with a real and public database indicate that support vector machines, logistic regression and stacking ensemble methods, trained with both attributes extracted from the text messages and posting information, are promising for the task of blocking undesired comments.
Proceedings ArticleDOI
Learning to Block Undesired Comments in the Blogosphere
TL;DR: This paper presents a comprehensive analysis of machine learning techniques applied to automatically detect undesired comments posted on blogs, and indicates that support vector machines and logistic regression are promising in the task of filtering unwanted comments.
Journal ArticleDOI
MDLText e Indexação Semântica aplicados na Detecção de Spam nos Comentários do YouTube
TL;DR: In this paper, an artigo avalia um metodo de classificacao baseado no principio da descricao mais simples e compara os resultados with os de metodos tradicionais de aprendizado online.