Comparative Performance Analysis of Techniques for Automatic Drug Review Classification

Year 2018, , 485 - 490, 28.12.2018

Alper Kürşat Uysal

https://doi.org/10.18466/cbayarfbe.481096

Cited By: 1

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.

Keywords

Pattern recognition, text classification, drug reviews, feature selection

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