**1. Introduction**

Since the world has been inundated with the increasing amount of tourist data, tourism organizations and business should keep abreast about tourist experience and views about the business, product and service. Gaining insights into these fields can facilitate the development of the robust strategy that can enhance tourist experience and further boost tourist loyalty and recommendations. Traditionally, business rely on the structured quantitative approach, for example, rating tourist satisfaction level based on the Likert Scale. Although this approach is effective to prove or disprove existing hypothesis, the closed ended questions cannot reveal exact tourist experience and feelings of the products or services, which hampers obtaining insights from tourists. Actually, business have already applied sophisticated and advanced approaches, such as text mining and sentiment analysis, to disclose the patterns hidden behind the data and the main themes.

Sentiment analysis (SA) has been used to deal with the unstructured data in the domain of tourism, such as texts, images, and video to investigate decision-making process [1], service quality [2], destination image and reputation [3]. As for the level of sentiment analysis, it has been found that most extant sentiment analysis in the domain of tourism is conducted at document level [4–7]. Document-based sentiment analysis (DBSA) regards the individual whole review or each sentence as an

independent unit and assume there is only one topic in the review or in the sentence. However, this assumption is invalid as people normally express their semantic orientation on different aspects in a review or a sentence [8]. For example, in the sentence "we had impressive breakfast, comfortable bed and friendly and professional staff serving us", the aspects discussed here are "breakfast", "bed" and "staff" and the users give positive comments on these aspects ("impressive", "comfortable" and "friendly and professional"). Since the sentiment obtained through DBSA is at coarse level, aspect-based sentiment analysis (ABSA) has been suggested to capture sentiment tendency of finer granularity.

To obtain the sentiment at the finer level, ABSA has been proposed and developed over the years. ABSA normally involves three tasks, the extraction of opinion target (also known as the "aspect term"), the detection of aspect category and the classification of sentiment polarity. Traditional methods to extract aspects rely on the word frequency or the linguistic patterns. Nevertheless, it cannot identify infrequent aspects and heavily depends on the grammatical accuracy to manipulate the rules [9]. As for the detection of sentiment polarity, supervised machine learning approaches, like Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM). Although machine learning-based approaches have achieved desirable accuracy and precision, they require huge dataset and manual training data. In addition, the results cannot be duplicated in other fields [10]. To overcome these shortcomings, ABSA of deep learning (DL) approaches has the advantage of automatically extracting features from data [9]. Extant studies based on DL methods in tourism have investigated and explored tourist experiences in economy hotel [11], the identification of destination image [12], review classification [13]. Although DL methods have been applied in tourism, ABSA in tourism is scant. Therefore, this study reviewed sentiment analysis at aspect level conducted by DL approaches, compared the performance of DL models, and explored the model training process.

With the references of surveys about DL methods [9, 14], this study followed the framework of ABSA proposed by Liu (2011) [8] to achieve the following aims: (1) provide an overview of the studies using DL-based ABSA in tourism for researchers and practitioners; (2) provide practical guidelines including data annotation, preprocessing, as well as model training for potential application of ABSA in similar areas; (3) train the model to classify sentiments with the state-of-art DL methods and optimizers using datasets collected from TripAdvisor. This paper is organized as follows: Section 2 reviews the cutting-edge techniques for ABSA, studies using DL for NLP tasks in tourism, and research gap; Section 3 presents the annotation schema of the given corpus and DL methods used in this study; Section 4 describes the details of annotation results, model training, and the experiment results. Section 5 provides the conclusions and future extensions.
