We will all be very sorry to say goodbye to Larissa when she soon returns to Universidade Federal de Minas Gerais (UFMG) in Brazil. This week, Larissa gave the group a talk which outlined her research and the work she has been doing in Wolverhampton for the past year.

Title: Mining Short Text data using Parallel programming

Abstract: This work describes the classification of texts as being either crime-related or non crime-related. Given the spontaneity  and popularity of Twitter we collected some posts related with crime and criminology, in the state of São Paulo-SP Brazil. However, this data set is not a collection of crime reports. As the web language is characterized by diversity including flexibility, spontaneity and informality we need a classification rule to filter the documents which really are in the context. The proposed methodology works in a two step framework. In the first step we partition the text database into smaller data sets which define text collections with characteristics (not necessarily directly observable) which allow a better classification process. This enables the usage of parallel computing which decreases the time process required for the technique execution. Later on each subset of the data induces a distinct classification rule with a Supervised Machine Learning technique. For the sake of simplicity we work with KMeans and KMedoids and linear SVM. We will present our results in terms of speed and classification accuracy using various feature sets, including semantic codes.