A Multilingual Sentiment Analysis Model in Tourism

Abstract

In recent years, scholars have dedicated significant attention to the field of sentiment analysis. A substantial volume of feedback shared by tourists on social networking platforms, notably on Tripadvisor, manifests as reviews. The tourism sector stands to gain valuable insights from sentiment analysis applied to such reviews. Despite the extensive body of research in sentiment analysis, scant attention has been directed toward multilingual sentiment analysis, particularly within the domain of tourism. This is noteworthy given the inherently multilingual and global nature of the tourism industry. This study aims to address this gap by presenting a comprehensive multilingual sentiment analysis conducted on Tripadvisor reviews. The sentiment analysis model is crafted using various layers of a neural network. We introduce an augmented Attention-based Bidirectional CNN-RNN Deep Model (Extended ABCDM). Comparative analysis reveals that the multilingual model attains a superior F1 measure of 0.732, outperforming previous models.