Evaluating customer emotions in coffee shop reviews using machine learning

Abstract

Sentiment analysis is a crucial technique for understanding and classifying the emotions expressed in written content. Given the widespread use of social media, these platforms have become an essential method for gathering customer feedback in several industries, including the café sector. This research introduces a method that uses the Soft K-means clustering algorithm to automatically evaluate sentiment in response to the growing online presence of coffee establishments. This approach is used to categorize customer evaluations on a famous coffee shop review website. The sentiment analysis of coffee shop reviews utilizes many machines learning methods, including Naïve Bayes, Gradient Boosting Machines (GBM), K-Nearest Neighbor, Support Vector Machines, Logistic Regression, and Random Forest. Furthermore, this paper proposes an innovative ensemble learning technique that integrates these six classifiers to enhance forecast accuracy. The efficacy of various tactics in efficiently capturing the ideas of coffee shop patrons is thoroughly compared.