(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Tourism sector is one of very important sector in the economy of any country as it provides them a chance to earn foreign exchange at a low social cost. In India, tourism contributed around 9.2% to its GDP in 2018. There is always an effort to increase tourist inflow in a country as there are several related businesses that flourish because of inflow of tourists. Therefore, it becomes critical that tourist expectations are met to their satisfaction and they plan a revisit and/or recommend the same to their friends and family members thus bring in more revenue. To know customer feedbacks, a formal feedback is/can be taken. However, tourists do give write-ups and reviews about a place, hotel, and restaurants that they have visited in a country on travel related websites such as trip advisor, blogs on incredible India etc.. Since these reviews and write ups are generally assumed to be written by a neutral travellers and provided first-hand account of their experiences, therefore, tourists all over the world take these reviews seriously to plan their future travel, stay and decide other preferences. Hotel authorities and other travel related businesses can use these reviews to improvise their services to meet customer expectation better. If they could analyse all the reviews given at different places about their services, then they can possibly provide best services leading to better ratings for their businesses. In this paper the effort has been made to analyse the reviews given by international tourists at various hotels and other travel related businesses about the destinations of rural India. The data about online reviews has been scrapped from various travel related websites and blogs. Further, a supervised machine learning technique has been used to classify the sentiments of the and hence suggest the areas of improvement for future business.