Opinion Mining Analysis on Online Product Reviews Using Naïve Bayes and Feature Selection

For profit companies that are centered on customers, every opinion of them is very important because knowing this opinion will be able to help the company to consider in making decisions about whether a product will be added to the production or not. It is necessary to know whether this opinion is positive or negative. This study discusses the classification of positive and negative opinions on online products using Naïve Bayes method. And to overcome a large number of datasets (having a lot of features/words) for increasing the accuracy of Naïve Bayes, it is combined with feature selection. In this case, the feature selection technique used is Chi-Square. This research is devoted to comparing system performance to see the performance of feature selection on small and large datasets. From the results of these tests, it is evident that feature selection combined with Naïve Bayes results in better system performance than not using feature selection for datasets that have many word features (large datasets) which can increase the accuracy around 11%. On the other hand, the use of feature selection has less effect on small datasets.