Verbal intelligence identification based on text classification

This paper analyses and compares term weighting methods for automatic verbal intelligence identification from speech. Two different corpora are used; the first one contains monologues on the same topic; the second one contains dialogues between two or three people. The problem is described as a text classification task with two classes: low and high verbal intelligence. Seven different term weighting methods were applied for text classification using the k-NN algorithm. The best result is obtained with the Confident Weights method as a term weighting method for the dialogue corpus. The best classification accuracy equals 0.80 and the best macro F1-score equals 0.79. The numerical results have shown that highest scores can be obtained when using a very small number of terms which characterize only the class of higher verbal intelligence.