**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 usergenerated 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.

**Keywords:** Deep learning, Aspect-based Sentiment Analysis, User-generated content, Gated Recurrent Neural Network
