Intelligent Tourist System For The 21st Century

Abstract

Travel Industry is always at the forefront of technology adoption. Most of our researchers and town planners have preferred the method of machine learning approach for tour recommendation models. Some traditional methods have achieved certain levels of success in tourism research, but sometimes artificial neural network (ANN) and regression analysis techniques give better results. Our objective is to investigate the different ways in which the machine learning models can be applied in tourism prediction problems and to show the performance of machine learning methods. In this paper we have an intelligent tourist system using ML/AI that has been modeled based on various features using Sentiment analysis and can be performed over these reviews which will be helpful to find tourist place popularity. Based on sentiment analysis results, tourists can easily decide their own tour destination to be visited. We have categorized the places based on their preference like the most visited or least visited family trip or friend’s trip and so on. The dataset was collected from various tourism review websites. We performed a comparative study of feature extraction algorithms that is Count Vectorization, TFIDF Vectorization, along with classification algorithms like Naive Bayes, Support Vector Machine, and Random Forest algorithm. With these results, a recommendation system has been built which would map an individual user’s interests to the highest rated tourist places and generates a tour plan satisfying the user’s needs.