Implicit Sentiment Analysis of Hotel Comments for Disaster Management: A Comparative Study of Machine Learning, Deep Learning, and BERT Model

Analyzing sentiment in hotel reviews is important for effective disaster management. Understanding and categorizing guests’ opinions and emotions about their hotel experiences can help identify potential issues promptly and enable hotels to respond to concerns, especially during crises such as natural disasters, health emergencies, or other disruptive events. Implicit sentiment analysis, an important task in Natural Language Processing (NLP), involves analyzing sentiments not explicitly stated in text documents, such as hotel comments. This form of sentiment analysis considers the broader context, tone, and linguistic cues present in the text to uncover underlying attitudes and feelings. By analyzing sentiment in guest reviews, hotels can better prepare for and mitigate the impact of disasters by addressing common concerns and improving overall service quality. To explore the application of sentiment analysis techniques and extract valuable insights from customer feedback, we used two datasets: A and B. We collected reviews from Algerian hotels in dataset A. Dataset B contains 38,932 reviews from hotels and restaurants from websites such as Booking and TripAdvisor. Because of the limited size of dataset A, we applied only machine learning models such as Naive Bayes, Decision Trees, Logistic Regression, and Support Vector Machines (SVM). The Support Vector Machine (SVM) achieved the highest accuracy with 90.9%. Additionally, we compared different frameworks, including machine learning, deep learning, and transformer models, using dataset B. The results show that the Long ShortTerm Memory (LSTM) model outperformed other approaches, yielding an accuracy of 93.76%.