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Classification of Customer Satisfaction for the Development of Hospitality Businesses

Talekar, Ruchira (2020) Classification of Customer Satisfaction for the Development of Hospitality Businesses. Masters thesis, Dublin, National College of Ireland.

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Customer perception is helpful in the development of hospitality businesses and customer reviews are the source to know the satisfaction of the customer. Classification of customer satisfaction can help hospitality businesses like a restaurant for their improvement as per the customer’s view and can improve negative services according to it. This research is based on big data handling and natural language processing. Research has analyzed the reviews of customers given for multiple hospitality businesses and classifying their sentiments into three classes, positive, negative, and neutral to predict customer satisfaction. The datasets used for the research are customer tweets using twitter streaming API and Yelp organization data, which contains various factors like, customer reviews, business name and id, states, ratings, etc. For the classification of sentiments, random forest, support vector machine and multinomial naïve Bayes model have been used. While k means algorithms have been implemented to group similar customers together. As compared to naïve Bayes, the SVM algorithm has shown more accuracy. But the random forest is giving the best accuracy i.e. 90% by performing the job of feature selection. This classification can be helpful for business development and for making managerial decisions for customer satisfaction.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 21 Jan 2021 11:30
Last Modified: 21 Jan 2021 11:30

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