Sentiment Analysis of Madura Tourism in New Normal Era using Text Blob and KNN with Hyperparameter Tuning

Tourism during the Covid-19 pandemic has paralysis, even though tourism is a source of regional income. In the new normal period, tourism began to rise again. Madura Tourism Sentiment Analysis is needed for regional parties and tourism developers to find a public opinion about tourism places in Madura that have been vacuumed for a long time. The dataset used is opinion data on Twitter for nature, culinary and religious tourism in Madura. Data was taken during the New Normal period between April 2020 to August 2021. This research compared Manual Lexicon Based and TextBlob for labeling data. TF-IDF for term weighting. SVM, Naïve Bayes, and KNN methods with Tuning Parameters are compared for classification methods in sentiment analysis. Based on this research, the best Accuracy value is 94% for SVM Method or KNN Method using Manhattan measure and K-Value = 1. The most positive labels are obtained for three tourism categories: nature, culinary, and religious.