Early text classification: a Naïve solutionManuscript submitted to SIAM International Conference on Data Mining 2016. This work was supported by CONACyT grants No. CB-2014-241306 (Clasificación y Recuperación de Imágenes Mediante Técnicas de Minería de Textos) and PN-2015 (Caracterización de usuarios en redes sociales: hacia un enfoque multimodal y multidominio).
Table 1: Data sets considered for experimentation. Red. V. is the number of terms when a reduced vocabulary is used.
['[BOLD] Text categorization  [BOLD] Data set', '[BOLD] Text categorization  [BOLD] Classes', '[BOLD] Text categorization  [BOLD] Terms', '[BOLD] Text categorization  [BOLD] Red.V.', '[BOLD] Text categorization  [BOLD] Train', '[BOLD] Text categorization  [BOLD] Test']
All of the data and our code will be made available under request for future comparisons. In the subsections below we provide details on each data set and report the corresponding experimental results obtained with them. Text data sets were processed as follows First we analyze the performance of early naïve Bayes on thematic text classification. We now evaluate the performance of ENB on the task of sexual predator detection. The problem approached in the original competition was to identify sexual predators from many chat conversations. However, in this work, we approach the problem of detecting conversations with potential sexual predators in it. Our ultimate goal, on the other hand, is to detect, as early as possible, conversations in which a sexual predator is involved, in such a way that sexual-attacks can be prevented and an alert for parents/police officers can be emitted.