Today scientists are inundated by the plethora of works that may or may not be relevant to their research interests and needs. One of the concepts proposed for overcoming this problem is Argumentative Zoning. It classifies information within scientific papers by assigning labels to sentences according to their rhetorical role. In a previous work, Support Vector Machines with RBF kernels are shown to give the best results when compared to other methods for the task of Argumentative Zoning. This paper aims to investigate the influence of further treatment on their performance, namely, using grid search for finding optimal parameter values, both under and without feature selection. Experiment results show that feature selection generally gives higher mean accuracy value when compared with when all features are used, averaging at 65.36% with a maximum value of 68.10%. However, when feature selection is not performed, using parameter values from the grid search, accuracy may reach 73.95%, although the average performance is lower. These results indicate that for the task of Argumentative Zoning with SVM RBF classifiers, good selection of parameter values is more important when compared feature set optimization.