Evaluating customer emotions in coffee shop reviews using machine learning

Marcel Merimee Bakala Mboungou *, Shunxiang Zhang and Iqra Yamin

Anhui university of science and technology, school of computer science and engineering, China.
 
Review
International Journal of Science and Research Archive, 2024, 12(01), 820–827.
Article DOI: 10.30574/ijsra.2024.12.1.0895
Publication history: 
Received on 10 April 2024; revised on 19 May 2024; accepted on 22 May 2024
 
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.
 
Keywords: 
Sentiment Analysis; Ensemble learning; Coffee shop Reviews; Machine learning
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0