## 1 Introduction  
Understanding people's opinions, intentions, and behaviours toward a service or product has been critical for various public and private organisations. Traditional research methods for understanding people's opinions and collecting data rely on instruments such as conventional questionnaire-based surveys, interviews, case studies, and focus groups. While these methods offer many advantages, they often suffer from a small study sample size, interviewers' and respondents' bias, and incomplete responses [1]. Social media networks, such as Facebook, Twitter and Web 2.0, were used by locals, tourists, travellers, consumers, and reviewers (hereinafter referred to as users) to virtually share their reviews, comments, photos, videos, emotions, and opinions about anything they experienced in their daily lives. These \"electronic word-of-mouth\" contents (e-WOM) became a powerful source of information, which is believed to influence consumers' decision-making processes, attitudes, and trust [2, 3]. E-WOM are not controlled by product and service providers and their marketing organisations. Therefore, they are considered less biased and more 'trustable' opinions by the public [4]. The power of e-WOM created a new dynamic of influence and persuasion towards various industries, including tourism, given that the tourism industry sells intangible products, which are difficult for consumers to evaluate pre-purchase [5]. As a result, e-WOM became a valuable source of data for studying tourists' sentiments, emotions, opinions, intentions, and behaviours, opening opportunities for innovative data-driven destination monitoring and management [2; 6]. Sentiment in the tourism context is a degree (measure) of feelings (attitudes) towards a tourism destination, service, or product, or an aspect of them, which is embedded in related textual reviews, comments, photos, or videos by tourists and/or marketing organisations [7]. On the other hand, emotion is a human feeling, such as being furious, joyful, angry, cheerful, or sad. Emotion analysis or detection is the way that human emotions are detected from data; for example, text and reviews [8; 9; 10; 11]. In the context of tourism, it is beneficial to analyse emotion and sentiment together as such information will give product and service providers and their marketing organisations a good understanding of how tourists perceive their products and services and what areas need further improvements. However, in many previous studies, sentiment and emotion analyses were often carried out independently. This study will address this issue by incorporating emotion and sentiment analysis under a single framework. Due to the large volume of e-WOM data generated daily [12], there is a growing need for a system that is capable of automatically processing and analysing e-WOM for sentiments, emotions, and other information [5; 8; 10; 13; 14]. In the last decade, researchers proposed different techniques to perform sentiment and emotion analysis on big data [7; 8; 10]. Natural language processing (NLP), text mining, automatic text categorisation, and summarisation are among the techniques used by researchers to automatically process, classify, and summarize e-WOM textual data, such as reviews and comments [12]. More recent studies used lexicon-based and machine learning techniques to measure overall sentiments and emotions associated with e-WOM [6; 7; 8; 15; 16]. Despite the progress in this area, none of the current sentiment analysis techniques were able to perform well across all domains and different contexts. As pointed out by [PERSON] et al. [15], sentiment results given by the existing techniques varied widely even across similar datasets, which indicates that the same content could be interpreted very differently depending on the choice of a sentiment analysis technique and the domain and context of the problem. A study by [PERSON] et al. [17] also suggested that sentiment polarity when analysing an opinion may be affected by the context in which the opinion was given.