Sentiment Analysis using SVM and Naïve Bayes Classifiers on Restaurant Review Dataset

Abstract

Consumer reviews on the food and services of a restaurant is a significant thing to monitor for restaurant businesses. Sentiment Analysis, having another name of Opinion Mining, is a technique that was used in order to identify people's opinions and attitudes towards certain subjects, and the most widely used application of sentiment analysis is analyzing consumer reviews of their products and services. This paper will assess sentiment analysis' performance with SVM and Naïve Bayes classifiers on a dataset of restaurant reviews. A grid search with different hyperparameters of the classifiers and feature selection methods is done to compare their effects on performance. Each model will be evaluated based on accuracy, F1 score, and confusion matrix. The trained models can be further finetuned to aid restaurant businesses in tracking their business performance and reputation.