Sentiment Analysis on Google Reviews Using Naïve Bayes, K-Nearest Neighbors, and Logistic Regression to Improve Novotel Services

The application of artificial intelligence (AI) has been widely used in various industrial sectors, including the hospitality industry. One of the applications that is widely used in the hospitality industry is sentiment analysis. Sentiment analysis is carried out by analyzing feedback data from hotel guests or customers. The results of this sentiment analysis are important for decision makers to improve and improve their services. This study aims to obtain sentiment analysis results from Novotel hotel Google reviews based on machine learning by comparing three algorithms, namely Naïve Bayes, K-Nearest Neighbors (KNN), and Logistic Regression. The stages carried out in this study are data collection, data labeling, exploratory data analysis (EDA), data preprocessing, text representation, data sharing, modeling, model training, model evaluation, selection of the most accurate model, visualization of the most accurate model, interpretation of results and writing research reports. The dataset used was 1200 reviews, only 1190 reviews were used in the analysis. From the training results, the model produced by the Logistic Regression algorithm was the most accurate, namely 94.54% with unigrams (n = 1). Here are the results of each category, positive as many as 723 reviews (60.76%), negative as many as 218 reviews (18.32%), and neutral as many as 249 reviews (20.92%). Thus, most of the sentiment towards the service is positive, but some services need to be fixed and improved for customer satisfaction. The next research, the research area is expanded and the use of Deep Learning.