Detection and Prediction of Customers Feedback on Hotel Businesses using Sentimental Machine Learning and Data Mining Algorithms

Sentimental analysis has established enormous improvement in recent years that not even a private field has been left from its grips. The hotel business today requires a good analysis on the feedback gotten from customers so that great decision could be made. The motivation behind this research is to ensure that customers are provided with satisfactory goods and services and the hunger for accurate decision making and achieving rapid growth within the hotel business. The aim of the research is to adopt sentimental machine learning and data mining algorithm in identifying, predicting and analyzing Customers feedback on hotel businesses while the objectives includes: to analyze customers feedback so as to identify lapses in service delivery in hotel business, to provide accurate and reliable information for top decision making within the organization and hence make accurate future forecast. Data mining methodology called sentimental analysis was adopted while implementing the aspect-base sentiment modeling tool. The data was analyzed with R and SAS Enterprise Miner. The experiment was done using twenty (20) different hotel/restaurants sourced from kaggle repository. The result after the experiment shows that the use of aspect-based sentimental analysis is the best and more accurate method suitable for data prediction and hence has more percentage acceptance level of performance when it comes to opinion mining peoples opinion on some issues, therefore it could be adopted for future use by various organizations to understand customers view on their products and services so that decision can be made accurately.