Predicting the political spectrum with Support Vector Machine

Support Vector Machines (SVMs) have gained considerable popularity over the last two decades for binary classification. This paper concentrates on a recent optimization approach to SVMs, the SVM majorization approach, or SVM-Maj for short. This method is aimed at small and medium sized Support Vector Machine (SVM) problems, in which SVM-Maj performs well relative to other solvers. To obtain an SVM solution, most other solvers need to solve the dual problem. In contrast, SVM-Maj solves the primal SVM optimization iteratively thereby converging to the SVM solution. Furthermore, the simplicity of SVM-Maj makes it intuitively more accessible to the researcher than the state-of-art decomposition methods. Moreover, SVM-Maj can easily handle any well-behaved error function, while the traditional SVM solvers focus particularly on the absolute-hinge error. In this paper, the SVM-Maj approach is enhanced to include the use of di_erent kernels, the standard way in the SVM literature for handling nonlinearities in the predictor space. In addition, the R package SVMMaj is introduced that implements this methodology. Amongst its features are the weighting of the error for individual objects in the training dataset, handling nonlinear prediction through monotone spline transformations and through kernels, and functions to do cross validation. This paper investigates the practicability of using Support Vector Machine as an automatic Machine Learning Technique on predicting the political spectrum of a person based on their choice of words in political issues. For this research, the statements of the parliamentarians in the plenary meetings of the House of Representatives have been used. Using the term frequency of the selected features in the model, it was possible to obtain a hit rate of up to nearly 70%, which was higher than the hit rate obtained by purely majority voting.