Tourism Mobile App With Aspect-Based Sentiment Classification Framework for Tourist Reviews

Tourist reviews are information sources for travelers to know about tourist places. Unfortunately, some reviews are irrelevant and become noisy data. Aspect-based sentiment classification methods have shown promise in suppressing the noise. However, not much research has been done on automatic aspect identification, and identification of implicit, infrequent and co-referential aspects, resulting in misclassifications. This paper presents a framework of aspect-based sentiment classification that will not only identify the aspects very efficiently but can perform classification task with high accuracy. The framework has been implemented as a mobile app that helps tourists find the best restaurant or hotel in a city, and performance has been evaluated by conducting experiments on real-world datasets with excellent results (85% identification and 90% classification).