Sentiment Analysis of Raja Ampat Tourism Destination Using CRISP-DM: SVM, NBC, DT, and k-NN Algorithm

Abstract

This study presents a sentiment analysis of tourists' opinions on Raja Ampat Tourism Destination using data mining techniques. The study collected data from Tripadvisor and processed it through sentiment classification. The algorithms used in the analysis were Support Vector Machine, Naive Bayes Classifier, Decision Tree, and k-Nearest Neighbor. The study followed the Cross-Industry Standard for Data Mining methodology and went through several stages such as business and data comprehension, data preparation and cleaning, feature selection, modeling, model evaluation, result presentation, deployment, and maintenance. The study's findings revealed that visitors generally had positive opinions about Raja Ampat's tourism attractions, particularly cultural diversity, and undersea beauty. The Decision Tree algorithm showed the highest accuracy value of 99.12%, precision of 98.96%, recall of 99.34%, AUC of 0.991, and f-measure of 99.13%. SVM also had excellent performance with an accuracy value of 100%, precision of 100%, recall of 100%, AUC of 1.000, and f-measure of 100%. The study concludes that Decision Tree and SVM, with the assistance of SMOTE operators, are the best algorithms for sentiment analysis in this context.

Keywords

• sentiment analysis
• svm
• nbc
• dt
• k-nn
• raja ampat