Comparative Performance Analysis of Techniques for Automatic Drug Review Classification

Abstract

This study analyses the effectiveness of six text feature selection methods for automatic classification of drug reviews written in English using two different widely-known classifiers namely Support Vector Machines (SVM) and naïve Bayes (NB). In the study, a recently published public dataset namely Druglib including drug reviews in English was utilized in the experiments. For evaluation, Micro-F1 and Macro-F1 success measures were used. Also, 3-fold cross-validation is preferred to perform a fair evaluation. The feature selection methods used in the study are Distinguishing Feature Selector (DFS), Information Gain (IG), chi- square (CHI2), Discriminative Features Selection (DFSS), Improved Comprehensive Measurement Feature Selection (ICMFS), and Relative Discrimination Criterion (RDC). However, experiments were performed using two settings in which stemming was applied and not applied. Experiments indicated that ICMFS feature selection method is generally superior to the other feature selection methods according to the overall highest Micro-F1 and Macro-F1 scores achieved on drug reviews. While the highest Micro-F1 score was achieved with the combination of NB classifier and ICMFS feature selection method, the highest Macro-F1 score was achieved with the combination of NB classifier and DFSS feature selection method. The highest Micro-F1 and Macro-F1 scores were achieved for the cases that stemming algorithm was not applied.