Sentiment analysis or opinion mining is a process of classifying
opinions, usually from a text, toward a particular issue, to be positive,
negative, or neutral. Nowadays, due to high number of social media users such
as twitter, the opinion of social media users are often used to determine the
public opinion. This can be used to find out the response of users to a particular
candidate in an election or even to predict the election results. One of the
challenges of using a document from social media, such as tweets, is the high
number of attributes used in comparison to the length of documents, which is
usually very short. In addition, the users tend to use informal languages in
their tweets. In this paper, we propose to use particle swarm optimization (PSO)
and Information Gain to select most appropriate attributes from documents and
use support vector machine (SVM) as the classifier. We develop a sentiment
analysis system for the election of West Java Governor. The experimental
results show that our proposed system achieve the accuracy of 94.80% and area-under-curve
(AUC) value of 0.98. Our results also show large improvements are achieved as
the results of using PSO dan Information gain compared to without using them.
Peer Reviewer