Using Transfer Learning Approach to Implement Convolutional Neural Network model to Recommend Airline Tickets by Using Online Reviews
Social Media provides an opportunity for people to share their idea about different aspects of life. Traveling is one of the essential aspects of life. In this paper, we use Bidirectional Encoder Representations from Transformers(BERT) for sentiment classification of online reviews to implement a Convolutional Neural Network model that can recommend airline tickets. Different sentiment classification models are compared based on people's online reviews from six online social platforms. The new model can classify airline tickets as an economical choice or not economical choice ticket based on various aspects of the trips, including airline customer satisfaction, travel destination, hotel information, restaurants, and tourist attractions. This work contribution is two fold: first, it examines the importance of choosing a transfer learning model for sentiment classification of online social platforms in the recommender system's prediction accuracy. Second, the implementation of the Convolutional Neural Network model, which can classify airline tickets based on data generated by multiple online social platforms. Using the natural language processing approach to use the transfer learning model to improve the CNN model's prediction accuracy is a new approach to use online social platforms to recommend economical airline tickets to consumers.
2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA