Sentiment Analysis of Restaurant Reviews Using Machine Learning Algorithms
Rahul Singh Chauhan, Sweta Bhandari, Himadri Vaidya · 2023
Customer input is crucial for organizations, and since social media is such a strong platform, it can be utilized to grow and improve company chances. However, this requires prompt analysis of the comments posted on social media. Consequently, this paper's primary objective is to analyze customer feedback. Data was gathered from Jerran, a social media platform for Arabic reviews. The collected data was initially analyzed to determine the emotions expressed in each comment, categorizing them as positive or negative. Subsequently, comments were automatically sorted using text classification algorithms based on assessments related to meal quality, surroundings, service, and price. A meticulously annotated dataset comprising approximately 4000 entries was used for training and testing purposes. Classification techniques such as Naive Bayes, Logistic Regression, and Support Vector Machine (SVM) were employed, and a comparison of their performance was conducted. The Support Vector Machine algorithm exhibited the most favorable outcomes.