A novel model for Document Representation

In this paper, we propose a novel model for Document Representation in an attempt to address the problem of huge dimensionality and vector sparseness that are commonly faced in Text Classification tasks. We conduct our experiments on data sets of Opinion Mining. We use as classifiers Support Vector Machines (SVM) and k-Nearest Neighbors (kNN). We compare the performance of our model with that of the classical representation based on Vector Space Model (VSM). Our experiments show that the effectiveness of our model depends on the used classifier. Results yielded by kNN when applying our model are the same as those obtained when applying the classical VSM. For SVM, results yielded when applying our model are typically lower than those obtained when using VSM. However, the gain in terms of time and dimensionality reduction is so promising since they are dramatically decreased by the application of our model. © 2013 IEEE.