• A. Prema
• S. Devi Durga
• S. Gayathri
• S. Harani Shree
• R. Siva Ranjani

Customer review sentiment research has a significant influence on a company's development plan. In the last ten years, the internet's development has generated vast amounts of data across all industries. There are presently a lot of reviews about the restaurant sector in online media, such as Twitter, Google reviews, etc., which have been growing daily due to customer actions. This industry needs to put more of an emphasis on customers by consistently improving customer service. Customer service begins with ensuring customer satisfaction. When classifying documents, sentiment analysis helps define whether they represent negative or positive opinions. Manually analyzing the data from social media evaluations was nearly impossible. Different data mining algorithms are utilized for assessment and analysis. The Nave Bayes (NB), Random Forest, Support Vector Machine, and K-Nearest Neighbor (KNN) sentiment classification algorithms are used in this study to analyze the sentiment of users' tweets about restaurants. The suggested system has the ability to analyze tweet sentiment. For consumer reviews, a sample of actual data was therefore taken from Twitter. The proposed system has been implemented using the Python programming language. The findings are consistent with the idea that patrons have positive opinions of both restaurants and other people, particularly those who own them. Accuracy and error rate are two popular performance evaluation metrics used to gauge the results.

Customer satisfaction, Naïve Bayes, Sentiment analysis. Support Vector Machine, Random Forest Classifier, K-Nearest Neighbor.

"Restaurant Review Using Sentiment Analysis in Social Media", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.p72-p77, May-2023, Available :http://www.jetir.org/papers/JETIR2305G10.pdf

"Restaurant Review Using Sentiment Analysis in Social Media", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppp72-p77, May-2023, Available at : http://www.jetir.org/papers/JETIR2305G10.pdf