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dc.contributor.authorAbd. Samad Hasan, Basari
dc.contributor.authorBurairah, Hussin
dc.contributor.authorAnanta, I. Gede Pramudya
dc.contributor.authorJunta Zeniarja
dc.date.accessioned2013-12-19T11:52:01Z
dc.date.available2013-12-19T11:52:01Z
dc.date.issued2012-11-20
dc.identifier.citationp. 545-552en_US
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/30617
dc.descriptionMalaysian Technical Universities Conference on Engineering and Technology (MUCET) 2012 organised by technical universities under the Malaysian Technical Universities Network (MTUN), 20th - 21st November 2012 at Hotel Seri Malaysia, Kangar, Perlis.en_US
dc.description.abstractNowadays, 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%.en_US
dc.language.isoenen_US
dc.publisherMalaysian Technical Universities Network (MTUN)en_US
dc.relation.ispartofseriesProceeding of the Malaysian Technical Universities Conference on Engineering and Technology (MUCET) 2012;
dc.subjectOpinionen_US
dc.subjectSVM-PSOen_US
dc.subjectOpinion miningen_US
dc.subjectSentimenten_US
dc.subjectSentiment analysisen_US
dc.subjectSupport Vector Machine (SVM)en_US
dc.titleOpinion mining of movie review using hybrid method of support vector machine and particle swarm optimizationen_US
dc.typeWorking Paperen_US
dc.contributor.urlabdsamad@utem.edu.myen_US
dc.contributor.urlburairah@utem.edu.myen_US


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