Sentiment Analysis of Customer Feedback Reviews Towards Hotel’s Products and Services in Labuan Bajo
Abstract
The feedback review column on the TripAdvisor website can process the sentiment analysis by classifying the positive and negative impressions of the consumer using machine learning, namely text mining. This study analyzes consumer sentiment for hotel products and services in Labuan Bajo based on feedback review data on the TripAdvisor website. Meanwhile, the algorithm used is Nave Bayes Classifier (NBC), Support Vector Machine (SVM), also SMOTE Upsampling for the k-Nearest Neighbor (k-NN) algorithm. The classification result shows 702 negative feedback reviews and 2531 positive feedback reviews. The evaluation of algorithm performance shows the accuracy of SVM is 78,30% and NBC is 78,29% compared with k-NN with 85,24% accuracy, using SMOTE Upsampling with a class precision description of 100% prediction negative (1784 true Neg & 0 true Pos) and 77,21% prediction positive (747 true Neg & 2531 true Pos). The class recall description also shows 70,49% true Negative (1784 pred Neg & 747 pred Pos) and 100% true Positive (0 pred Neg & 2531 pred Pos). These findings indicate that the k-NN algorithm shows the best result instead of the SVM and NBC algorithm, according to the sentiment analysis result of customer feedback reviews on hotel products and services in Labuan Bajo through TripAdvisor website.
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