2 research outputs found
Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization
Nowadays, online social media is online
discourse where people contribute to create content, share
it, bookmark it, and network at an impressive rate. The
faster message and ease of use in social media today is
Twitter. The messages on Twitter include reviews and
opinions on certain topics such as movie, book, product,
politic, and so on. Based on this condition, this research
attempts to use the messages of twitter to review a movie by
using opinion mining or sentiment analysis. Opinion mining
refers to the application of natural language processing,
computational linguistics, and text mining to identify or
classify whether the movie is good or not based on message
opinion. Support Vector Machine (SVM) is supervised
learning methods that analyze data and recognize the
patterns that are used for classification. This research
concerns on binary classification which is classified into two
classes. Those classes are positive and negative. The positive
class shows good message opinion; otherwise the negative
class shows the bad message opinion of certain movies. This
justification is based on the accuracy level of SVM with the
validation process uses 10-Fold cross validation and
confusion matrix. The hybrid Partical Swarm Optimization
(PSO) is used to improve the election of best parameter in
order to solve the dual optimization problem. The result
shows the improvement of accuracy level from 71.87% to
77%