Tareq  Al-Moslmi, Nazlia  Omar, Mohammed  Albared, Adel  Alshabi, 
    Enhanced Malay Sentiment Analysis with an Ensemble Classification
Machine Learning Approach,
    Journal of Engineering and Applied Sciences,
    Volume 12,Issue 20,
    2017,
    Pages 5226-5232,
    ISSN 1816-949x,
    jeasci.2017.5226.5232,
    (https://makhillpublications.co/view-article.php?doi=jeasci.2017.5226.5232)
    Abstract: Sentiment analysis is one of the challenging and important tasks that involves natural language
processing, web mining and machine learning. This study aims to propose an enhanced ensemble of machine
learning classification methods for Malay sentiment analysis. Three classification approaches (Naive Bayes,
Support vector machine and K-Nearest Neighbour) and five ensemble classification algorithms (Bagging,
Stacking, Voting, AdaBoost and MetaCost) were experimented to achieve the best possible ensemble model
for Malay sentiment classification. A wide range of ensemble experiments are conducted on a Malay Opinion
Corpus (MOC). This study demonstrates that ensemble approaches improve the performance of Malay
sentiment-based classification, however, the results depend on the classifier used and the ensemble algorithm
as well as the number of classifiers in the ensemble approach. The experiments also show that the ensemble
classification approaches achieve the best result with an F-measure of 85.81%.
    Keywords: Malay sentiment analysis;opinion mining;machine learning;classification;approaches achieve;sentiment-based