Trip Planner: A Big Data Analytics Based Recommendation System for Tourism Planning

Abstract

Foreign tourism has gained immense popularity in the recent past. To make a rational decision about the destination to be visited one has to go through variety of social media sources with very large number of reviews, which is a tedious task. Automated analysis of these reviews is quite complex as it involves non structured text data having slang terms also. Moreover, these reviews are pouring in continuously. To overcome this problem, this paper provides a Big Data analytics-based framework to make appropriate selection of the destination on the basis of automated analysis of social media contents based upon the adaptation and augmentation of various tools and technologies. The framework has been implemented using Apache Spark and Bidirectional Encoder Representation Transformers (BERT) deep learning models through which raw text review are analysed and a final score based on five metrics is obtained to recommend destination for visit.