Comparative analysis of hotel reviews using proposed LSTM based deep learning model with classical machine learning techniques

The purpose of sentiment analysis is to find the positive and negative reviews. Hotel reviews have been analyzed in this paper using some machine learning algorithms and further, the most efficient machine learning technique that is called deep learning method of LSTM has been implemented. Further, the comparison has been done and it is analyzed that how LSTM has overcome the classical machine learning methods as the output and efficiency of deep learning method is better than machine learning algorithms like Naive Bayesian Classifiers, SVM classifier and Decision Trees. The idea is to build a deep learning model using LSTM technique that works on hotel reviews in context of online tourism that gives better results in comparison of previous machine learning techniques. This model will help tourism industry to enhance business by analyzing the hotel reviews of customers. As Sentiment analysis is tremendously used in business field to improve the current products and services by taking the input in the form of opinions of customers regarding these services and hence is the case of hotel reviews analysis using LSTM to get the efficient and clear results that would definitely help the tourism industry to know the opinion of customers with more clarity and efficiently so that industry can provide better services to the customers in future.