TY  - JOUR
T1  - Enhanced Malay Sentiment Analysis with an Ensemble Classification
Machine Learning Approach
AU - Al-Moslmi, Tareq AU - Omar, Nazlia AU - Albared, Mohammed AU - Alshabi, Adel 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 20
SP  - 5226
EP  - 5232
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.5226.5232
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.5226.5232
KW  - Malay sentiment analysis
KW  -opinion mining
KW  -machine learning
KW  -classification
KW  -approaches achieve
KW  -sentiment-based
AB  - 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%.
ER  -