## 2 Literature Review
### Target-Oriented Sentiment Analysis  
Target-oriented sentiment analysis intends to classify sentiment polarities over a target, i.e., a point of interest such as an individual topic, product, service, landmark, or object in an opinion text. A target can be a single entity, such as a product or a service, but it can also be a complex hierarchy of sub-targets [36]. For example, a tourist destination is a target with different aspects (sub-targets), such as attractions, accommodation, transport, and natural environment, which maintain some relationship with each other. In this study, a hybrid annotation approach, combining the top-down coding process informed by the literature [36; 37; 38; 39] on key destination attributes with the bottom-up coding process through which new targets/aspects were added to the literature-informed list of attributes as they emerged in the annotation process were considered for destination structure and nine aspects, including attraction, accommodation, food and beverage, weather, people and culture, transport, holiday in general, shopping and gift, and city facilities, were identified. This is in line with the attributes found by [PERSON] et al. [38]. A challenge in target-oriented sentiment analysis is identifying targets, sub-targets, and their relationships. Based on the granularity of analysis, a target may refer to a concrete tangible entity or an abstract subject that was commented on in a post. Appropriate classification and modelling of targets help to determine more precise target-oriented sentiment. Relations between different aspects of a complex target are often modelled using ontology learning techniques [40]. Depending on the application and the grouping rules, the hierarchical relationship between a target and its aspects or sub-targets may differ. We will discuss the target identification and modelling process in more detail with examples later in the subsequent sections. To develop machine-based sentiment analysis methods to target specific topics and objects of a tourist destination, relevant datasets with sentiments annotated for the topics and objects of interest are required. Annotated datasets are crucial for training and evaluating the performance of machine-operated algorithms [7]. However, besides a small number of datasets of user-generated reviews (UGR), focusing mainly on domains other than tourism, such as news, movies, meeting minutes and reports [17; 36] there are only a few datasets constructed for the hospitality and tourism domain within a small set of areas including hotels [21; 22; 23; 27], airlines [25] and restaurants [41]. Data sources include Twitter [25; 26], Citysearch [41], Booking.com [22], and TripAdvisor [21; 22; 23]. More importantly, the analytical focus of these datasets is on the overall sentiment toward a tourism destination, service, or product rather than the sentiment toward a specific attribute of the above. Among the datasets mentioned above, only [PERSON]'s [21] dataset of restaurant and hotel reviews annotated targets for target-oriented sentiment analysis. To the best of our knowledge, no publicly accessible dataset is available that comprises annotated social media posts to provide the ground truth of sentiments toward a range of targets of a tourism destination, such as accommodation, food and beverage, attractions, and weather. Subsequently, there is also no existing method or scheme to annotate social media posts for target-oriented sentiment analysis. This study, therefore, proposes a method to annotate social media posts for target-oriented emotion and sentiment analysis and subsequently builds a publicly available annotated dataset from Twitter posts to bridge the research gap. Sentiment analysis is a fundamental task in natural language processing. Since the sentiments in a sentence can be complex and varied by different targets, target-oriented sentiment analysis is proposed to refine sentiment analysis. Target-based sentiment analysis aims to detect a target and sentiment polarity in a text.