TY  - JOUR
T1  - Comparative Performance Analysis of Techniques for Automatic Drug Review Classification
AU  - Uysal, Alper Kürşat
PY  - 2018
DA  - December
DO  - 10.18466/cbayarfbe.481096
JF  - Celal Bayar University Journal of Science
JO  - CBUJOS
PB  - Manisa Celal Bayar Üniversitesi
WT  - DergiPark
SN  - 1305-130X
SP  - 485
EP  - 490
VL  - 14
IS  - 4
LA  - en
AB  - This study analyses the effectiveness of six textfeature selection methods for automatic classification of drug reviews writtenin English using two different widely-known classifiers namely Support VectorMachines (SVM) and naïve Bayes (NB). In the study, a recently published publicdataset namely Druglib including drug reviews in English was utilized in theexperiments. For evaluation, Micro-F1 and Macro-F1 success measures were used.Also, 3-fold cross-validation is preferred to perform a fair evaluation. The featureselection methods used in the study are Distinguishing Feature Selector (DFS), InformationGain (IG), chi-square (CHI2), Discriminative Features Selection (DFSS), ImprovedComprehensive Measurement Feature Selection (ICMFS), and Relative DiscriminationCriterion (RDC). However, experiments were performed using two settings inwhich stemming was applied and not applied. Experiments indicated that ICMFSfeature selection method is generally superior to the other feature selectionmethods according to the overall highest Micro-F1 and Macro-F1 scores achievedon drug reviews. While the highest Micro-F1 score was achieved with thecombination of NB classifier and ICMFS feature selection method, the highestMacro-F1 score was achieved with the combination of NB classifier and DFSSfeature selection method. The highest Micro-F1 and Macro-F1 scores were achievedfor the cases that stemming algorithm was not applied.
KW  - Pattern recognition
KW  - text classification
KW  - drug reviews
KW  - feature selection
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UR  - https://doi.org/10.18466/cbayarfbe.481096
L1  - https://dergipark.org.tr/tr/download/article-file/606595
ER  -