Navigating Guest Sentiments: Comparative Analysis of Decision Tree and VADER for Hotel Review Analysis

These days, while making hotel reservations, more thought is given to hotel reviews. The views of hotels have grown throughout the past few years. Because of the conflicting assessments, analysis is necessary to identify whether a hotel is positive or negative. The process of analyzing digital text to determine if the emotional tone of the communication is neutral, positive, or negative is known as sentiment analysis. Sentiment analysis is a new field that automates the study of such data by combining Machine Learning (ML) with Natural Language Processing (NLP). Initially, in this research work, dataset is constructed by crawling Tripadvisor data regarding hotel reviews using the hotel review dataset with two distinct algorithms, VADER and Decision Tree. This work has shown that, in comparison to Decision Tree algorithms, VADER is easier to build and determines the sentiment of the dataset. Hence, comparison shows that the Decision Tree method, VADER’s accuracy on the hotel review dataset has slightly improved.