Sentiment Analysis of Online Reviews: A Machine Learning Based Approach with TF-IDF Vectorization

Abstract

Nowadays, online reviews wield considerable influence over consumer decision-making processes. Surveys show 84% of people compare their trustworthiness to recommendations from personal connections in these online reviews. Online reviews of services or destinations can significantly benefit the tourism industry. Therefore, our primary intent of this study is to leverage Machine Learning (ML) and Natural Language Processing (NLP) for sentiment analysis of hotel reviews in Jordan in order to assist both hotel owners and tourists. In this study, we proposed a ML-based approach using Support Vector Machine (SVM) and TF-IDF to perform sentiment analysis of hotel reviews into positive or negative. In addition, our experiments were performed using our real dataset, “JOHotelRating”, which was gathered in the Jordanian context. In the feature extraction stage, we utilized the Term Frequency-Inverse Document Frequency (TF-IDF) method. In the machine learning (ML) classification phase, we utilized various algorithms such as Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), Bernoulli’s Naïve Bayes (BNB), Decision Tree (DT), and Random Forest (RF). SVM with TF-IDF for feature extraction, emerged as the standout performer, achieving an impressive 97% accuracy in sentiment classification. Our proposed approach offers the hotel owners a time-saving method to identify positive and negative reviews, allow them to understand trends, and enhance the overall customer experience. On the tourist side, the study attempts to tackle the challenge of comprehending numerous reviews by providing sentiment analysis, ultimately aiding them in making better-informed decisions when selecting a hotel in Jordan.