Sentiment Analysis in Airline Tweets Using Mutual Information for Feature Selection

Social media is a media that many users need to be connected with other users in order to establish communication. One of the most widely used social media is Twitter. This Twitter contains opinions or short messages called tweets. The invited company also needs feedback from its customers to find out their view of the requested service. Therefore sentiment analysis is needed to collect sentiment classification of the company. This research uses a dataset from a collection of tweets about US Airlines. Because the dataset has been provided in Kaggle and already has several metadata so the experiment about feature selection can be done quickly. The features selection in this research uses the Mutual Information method. The method was chosen because it opposes the previous reference which the method is effective in correlation measurement from one attribute to another. The results obtained show that the training data created with features selected using complementary information has better. However, this mutual information when compared with the selection of other features such as Chi Square and Annova F to choose the old process and verify use for both methods.