Sentiment Analysis for Hotel Reviews with Recurrent Neural Network Architecture
Kürşat Mustafa KARAOĞLAN Volkan Temizkan Oğuz Findik
AbstractOnline marketing platforms have turned into large volumes of information and opinion for customers with the transition to Web 2.0. Customers refer to these resources in order to obtain information before they purchase a product and to reach the potential views of others about possible experiences. Businesses also need customer feedback to improve the services they provide and to explore which reviews are more valuable product specifications. In this study, Sentiment Analysis (SA) was performed with 2-pole (positive-negative) classification about hotel businesses on an opinion dataset created by users. Deep Learning based Recurrent Neural Network (RNN) architecture was used in these analyzes. With the results of the RNN architecture, the results of the classification based on score conditional and editorial interpretation were compared and it was observed that the performance of classification with RNN was successful.