Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 45, Pages: 1-9
Irina Pak* and Phoey Lee Teh
*Author for correspondence
Irina Pak Faculty of Science and Technology, Sunway University, 5, JalanUniversiti, Bandar Sunway, Subang Jaya, Selangor - 47500, Malaysia; [email protected]
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 measured by examining overall accuracy, recall, precision, kappa statistic and applying 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 in the online reviews. Data used is never been used before to explore the performance of machine learning classifiers.
Keywords: Comments, Machine Learning Classifiers, Online Reviews, Polarity, Sentiment Analysis
Subscribe now for latest articles and news.