Tourists Behavior Prediction through Online Reviews by Analyzing their Sentiments using Machine Learning Approach

Abstract

Listen

Translate article

Sentiment Analysis is that part of research area where different people opinions or sentiments are extracted in form of textual data from various websites. In this paper sentiment analysis has been described along with machine learning techniques on tourist’s reviews to see their behavior towards various tourist places, hotels and other services provided by tourism industry. Emotions in the form of tourist’s reviews extracted are interpreted and classified by preprocessing of data and further feature extraction is done through machine learning highly efficient technique called deep learning. In this paper, the proposed idea has been given to use deep learning methods like CNN, RNN and LSTM rather than using machine learning classical algorithms like SVM, Naive Bayes, KNN, RF etc. Also, comparison of various machine learning and deep learning techniques working on tourist sentiments has been done here in this paper to show that deep learning techniques analyze and classify emotions and polarity with deep layers efficiently where on the other hand classical algorithms of machine learning give results not better than deep learning techniques. In this way sentiment analysis has been done and the proposed idea of this research paper is change in the machine learning techniques or methods from classical algorithms to neural network deep learning methods which in future definitely will give better results to analyze deeply the sentiments of tourists to find out the liking and disliking of various tourist places, hotels and related tourism services that will help tourism business industry to work on the gap in existing services provided by them and system can become more efficient in future. Such improved tourism system will give benefits to tourists or users in terms of better services and undoubtedly it will help tourism industry to enhance business in future. Keywords—sentiment analysis, machine learning, deep learning, tourist reviews.