Can a troubled political era be detected by machine learning methods? An application on the End of Year speeches of the Italian Presidents

President Oscar Luigi Scalfaro took office in 1992, when the Italian political and judicial climate was highly turbulent. As his speeches may be considered a good source of information for grasping the transition between the so called "First" and the "Second" Republic, this study aims at analysing the 71 End of Year speeches pronounced by the 11 Presidents of the Italian Republic in order to check whether we are able to detect a clear-cut fracture within the corpus by means of supervised machine learning methods applied to texts. For this purpose, our corpus has been arranged in three different configurations to assess the performance of different classification tasks within the training set (cross-validation procedure) and to observe in which one of the three arrangements the algorithms perform better. For the final attribution task we chose the configuration that showed the highest accuracy and we split the set of speeches in a training set and a test set to observe how the algorithms assign the text chunks of the test set to the two classes (First or Second Republic). A comparison between the performance of the two different algorithms used in this work, i.e. Support Vector Machine (SVM) and Random Forest (RF) has been discussed throughout the whole study. With reference to previous studies, Scalfaro's speeches confirmed their role as transition messages and the maximum precision was reached by both algorithms when they were discarded by the two classes.