## 2 Literature Review
### Sentiment Analysis and Its Application in Tourism  
Information provided through a social media post is either objective (i.e., factual) or subjective (i.e., opinionated) [7]. Compared with non-opinionated objective posts on facts, evidence, and measurable observations, subjective posts are the explicit point of view, personal beliefs, feelings and emotions, and judgments about individuals, things or events, which may imply a positive, negative, or mixed feeling [7]. Opinions conveyed in social media posts can be evaluated using sentiment analysis. Sentiment analysis is a broad field of study, referred to as opinion mining or subjectivity analysis, with some connections to affective computing (computer recognition and expression of emotion) [19; 20]. Sentiment analysis is composed of knowledge and technologies from many disciplines, including linguistics, computer science, and natural language processing. In this paper, we refer to sentiment analysis as a computational process aiming to measure and evaluate consumers' and tourists' opinions, feelings, and emotions expressed in social media posts. The sentiment analysis literature specified quantitative and qualitative measurement schemes to measure sentiments, where the binary, ternary, and ordinal sentiment polarity classification schemes are the most widely used. The binary scheme labels a social media post as either \"positive\" or \"negative\". In this scheme, any post is subjective, i.e., it reflects a person's viewpoint and opinion. The ternary scheme tries to differentiate between objective and subjective posts, referring to objective posts as those containing facts, evidence, and measurable observations [7]. Objective posts will be classified as \"neutral\" in this scheme, while subjective posts are classified as either \"positive\" or \"negative\". Unlike the binary and ternary schemes, the ordinal scheme classifies a post using a rating scale or sentiment strength [21]. Methods for analysing the sentiment can be categorized mainly into dictionary-based, machine learning, and hybrid, where dictionary-based methods are commonly used in the tourism sector [15; 21; 22; 23; 24; 25]. The Valence Aware Dictionary for Sentiment Reasoning (VADER) is one of the dictionary-based methods. VADER was specifically proposed for sentiment analysis of Twitter data [24]. It combines a lexicon/dictionary and a series of intensifiers, punctuation transformations, and emoticons to compute the sentiment polarity of a post [7; 24]. On the other hand, machine learning methods rely on machine learning models to compute sentiments. Support Vector Machines (SVM) and Naive Bayes classifiers [22] are common machine learning models used for sentiment polarity classification. As its name suggests, a hybrid method combines machine learning and dictionary-based technique in one process to obtain the final sentiment polarity of a post [26; 27]. Sentiment analysis of e-WOM data gained attraction in the tourism industry as a method to explore tourist experiences. Studies were carried out in different contexts, including airlines [25; 28], hotels [29; 30], and sport events [31]. [PERSON] et al. [29] showed that sentiment analysis of e-WOM data can give a deeper understanding of what contributes to the overall rating score of a hotel. A similar observation was also reported by [PERSON] et al. [25], suggesting that using e-WOM data can give a deeper insight into tourist experiences beyond what we observed in the literature. Nonetheless, research in this direction remains preliminary, with a small number of works focusing on understanding the overall sentiment of a review or comment. Meanwhile, according to [PERSON] [21], a tourism-related social media post may contain information and opinion on multiple topics and objects of a destination, and the sentiment associated with each topic or object can be analysed individually.