Machine learning classifiers: Evaluation of the performance in online reviews

Pak, Irina and Teh, Phoey Lee * (2016) Machine learning classifiers: Evaluation of the performance in online reviews. Indian Journal of Science and Technology, 9 (45). pp. 1-9. ISSN 0974-5645

This is the latest version of this item.

[img]
Preview
Text
phoey lee teha.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1275Kb) | Preview
Official URL: http://indjst.org/index.php/indjst/article/view/10...

Abstract

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. 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. 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. 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.

Item Type: Article
Uncontrolled Keywords: machine learning classifiers; sentiment analysis; online reviews, comments; polarity.
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Sunway University > School of Science and Technology > Dept. Computing and Information Systems
Depositing User: Dr Janaki Sinnasamy
Date Deposited: 17 Feb 2017 09:18
Last Modified: 21 Feb 2017 09:57
URI: http://eprints.sunway.edu.my/id/eprint/474

Available Versions of this Item

Actions (login required)

View Item View Item