Machine Learning Classifiers: Evaluation of the Performance in Online Reviews

Abstract

Objectives: This paper aims to evaluate the performance of the machine learning classifiers and identify the most suitable classifier for classifying sentiment value. The term “sentiment value” in this study is referring to the polarity (positive, negative or neutral) of the text. Methods/Analysis: This work applies machine learning classifiers from WEKA (Waikato Environment for Knowledge Analysis) toolkit in order to perform their evaluation. WEKA toolkit is a great set of tools for data mining and classification. The performance of the machine learning classifiers was evaluated by looking at overall accuracy, recall, precision, kappa statistic and few visualization techniques. Finally, the analysis is applied to find the most suitable classifier for classifying sentiment value. Findings: Results show that two classifiers from Rules and Trees categories of classifiers perform equally best comparing to the other classifiers from categories, such as Bayes, Functions, Lazy and Meta. Novelty/Improvement: This paper explores the performance of machine learning classifiers in sentiment value classification of the online reviews. Data used is never been used before to explore the performance of machine learning classifiers.