**5. Conclusions and future extensions**

In this study, the hotel review dataset collected from TripAdvisor for aspect-level sentiment classification was first established. The dataset contains 5506 sentences in which the numbers of positive, neutral, and negative sentiment samples are 3032, 2986, and 2725, respectively. In order to study the effect of the fraction of sentiment samples on the model performance, four sub-datasets with a various fraction of sentiment samples were resampled from the TripAdvisor hotel review dataset as the train sets. The task in this study is to determine the aspect polarity of a given review with the corresponding aspects. To achieve a good predictive performance toward a multi-class classification task, attention-based GRU and LSTM (AT-GRU and AT-LSTM), as well as attention-based GRU and LSTM with aspect embedding (ATAE-GRU and ATAE-LSTM), were optimized with SGD, Adam, and AdaBelief and trained with epochs of 100, 300, and 500, respectively. Conclusions from these experiments are as follows:


*Tourist Sentiment Mining Based on Deep Learning DOI: http://dx.doi.org/10.5772/intechopen.98836*

This work includes the application of natural language processing technologies on the aspect-level sentiment analysis of the TripAdvisor hotel dataset, and there are still several extensions to be explored as follows:

