Tourist Sentiment Mining Based on Deep Learning

Abstract

Mining the sentiment of the user on the internet via the context plays a significant role in uncovering the human emotion and in determining the exactness of the underlying emotion in the context. An increasingly enormous number of user-generated content (UGC) in social media and online travel platforms lead to development of data-driven sentiment analysis (SA), and most extant SA in the domain of tourism is conducted using document-based SA (DBSA). However, DBSA cannot be used to examine what specific aspects need to be improved or disclose the unknown dimensions that affect the overall sentiment like aspect-based SA (ABSA). ABSA requires accurate identification of the aspects and sentiment orientation in the UGC. In this book chapter, we illustrate the contribution of data mining based on deep learning in sentiment and emotion detection.

Unveiling the Drivers of Chinese Tourists’ Visit Intentions Regarding Malaysia

“…Although sentiment analysis based on UGC can offer deep insights into tourists' emotional responses to negative news, this research relies on extensive foundational data, and sudden events often fail to generate sufficient commentary on social media in a short time frame [54,55]. Additionally, when evaluating travel destinations, Chinese tourists tend to use indirect feedback methods, even using anonymous social media platforms [56].…”