Sentiment Analysis of Hotel Reviews using Deep Learning Approaches

Abstract

The tourism industry is experiencing rapid growth and is becoming one of the fastest-expanding sectors. A considerable number of travelers now make hotel bookings and share their experiences on travel e-commerce platforms. Enhancing the quality of products and services within this industry can be accomplished by scrutinizing customer feedback. This research employs advanced deep learning techniques to discern subtle sentiments and glean insights from an extensive collection of hotel reviews. Deep neural networks, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), are widely acknowledged for their effectiveness in sentiment analysis. This study conducts a comparative examination of various network architectures, encompassing CNN, LSTM, CNN-LSTM, BiLSTM, and ConvBiLSTM, with the objective of pinpointing the most suitable approach. The experimental results are derived from datasets containing hotel reviews from Indonesia, obtained by crawling TripAdvisor. Notably, the LSTM model achieved an accuracy score of 96.42% on the Padma Hotel dataset and 85.31% on the Hard Rock Hotel dataset. The CNN-LSTM model demonstrated an accuracy of 85.87% on the Ayana Hotel dataset, while the BiLSTM model achieved 86.09% accuracy on the Pullman Hotel dataset. In assessing the performance of machine learning versus deep learning models, the analysis extends to commonly used IMDb datasets. The experimental results underscore the superiority of deep learning models over machine learning models across all evaluated metrics.