Sentiment analysis of facebook comments on indonesian presidential candidates using the naïve bayes method

Sentiment analysis is a very interesting research object to study. Sentiment analysis itself is one branch of textmining whose research focuses on the opinion of a text document. In this study, the author examines the sentiment analysis of facebook commentary of 2 Indonesian presidential real candidates in the 2014. Henceforth in this study, the two candidates are referred to as Presidential Candidate 1 and Presidential Candidate 2, where the numbering sequence is adjusted to the numbering of real election data of president in Indonesia. Here the author chooses to use Facebook comment data on several statuses posted on the 2 official accounts of the Indonesian presidential candidates, because Facebook is a social media that is widely used by Indonesians, it is evident that Facebook's social media ranks 3rd in Indonesia. In the process of classifying this text using the Naïve Bayes method because this method is very simple, has good performance in many domains and this method is very simple. But the Naïve Bayes method itself, has the disadvantage of being very sensitive to too many features, which can lead to low classification accuracy. To overcome the problems that exist in the Naïve Bayes method, this study uses a combination of feature selection methods, namely information gain and genetic algorithm, the two additional methods serve to improve the accuracy in Naïve Bayes classifier. In addition, in this study the author uses smile-forming character conversion, pre-processing of documents such as transform case, tokenization, Indonesian stopwords, Stemming, Token weighting, then classification and confusion matrix testing. This research produces a positive or negative text classification from Facebook comments. Then the measurement is based on the accuracy of Naïve Bayes before and after the addition of the feature selection method. The evaluation process uses 10 fold cross validation. From the results of the implementation and testing, the Naïve Bayes method with feature selection has an accuracy level of sentiment classification of 83.67% from the previous results of 60.00% here the researcher also displays the ROC curve.