Hasan Basari, Abd Samad and Burairah, Hussin and Pramudya Ananta, Gede and Zeniarja, Junta (2012) Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization. In: MUCET2012, 20-21 NOVEMBER 2012, HOTEL SERI MALAYSIA, KANGAR,PERLIS.
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Abstract
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%.
| Item Type: | Conference or Workshop Item (Paper) |
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| Subjects: | Q Science > Q Science (General) |
| Divisions: | Faculty of Information and Communication Technology > Department of Industrial Computing |
| Depositing User: | Dr. Abd. Samad Hasan Basari |
| Date Deposited: | 19 Apr 2013 03:46 |
| Last Modified: | 31 May 2023 14:48 |
| URI: | http://eprints.utem.edu.my/id/eprint/6796 |
| Statistic Details: | View Download Statistic |
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