Analisis Sentimen Review Hotel Menggunakan Metode Deep Learning BERT

Abstract

The COVID-19 pandemic has resulted in declining tourism visits and hotel occupancy. Hoteliers must monitor visitor lifestyles to sustain their businesses. One way to achieve this is by understanding the sentiment of hotel visitors through review analysis, enabling better decision-making regarding service and business aspects in the hotel industry. This research applies the natural language processing deep learning model BERT to analyze positive and negative sentiments from hotel visitor reviews in Indonesia. The BERT model undergoes a pre-trained and fine-tuned process to produce accurate sentiment analysis. Evaluation results demonstrate that the fine-tuned SmallBERT model performs well, trained on a dataset of 515k hotel reviews for five epochs. The SmallBERT model achieves an accuracy of 91.40%, precision of 90.51%, recall of 90.51%, and an F1 score of 90.51% when evaluated with manually labelled datasets. Visualizations of the predominantly positive sentiment comparisons are conducted using Tableau.