## 1 Introduction
For example, the adjective \"lazy\" has a 'negative' connotation when it is associated with \"staff\" in the context of \"lazy staff\". On the other hand, the \"lazy\" adjective might be interpreted as 'positive' when someone is \"having a lazy afternoon on the beach\". Ambiguity is the beauty of a natural language, but at the same time, it poses a difficult challenge for any machine-based sentiment analysis technique. Moreover, sentiment and emotion analysis was mainly carried out at the post level, while a post often contains multiple topics (targets) [7]. A natural approach to overcoming the ambiguity challenge is to limit the context in which sentiment analysis is performed and to develop a context-aware sentiment analysis technique for the context. We, therefore, proposed a method to analyse emotion and sentiment for individual topics/targets under a single framework. We showcase our framework using a limited number of tweets in relation tourism. In particular, we proposed a method for conducting a so-called automatic target-oriented emotion and sentiment analysis of e-WOM. The method is developed to perform sentiment analysis and emotion detection in the context of tourism destination management. The term 'target' refers to a specific point of interest at the destination. Limited data collected from the Gold Coast of Australia as a tourist destination are used to illustrate the proposed system. We conducted this study in two phases. The first phase aimed to present an annotation approach and construct an annotated dataset for training and assessing the performance of target-oriented emotion and sentiment analysis algorithms. In addition, it also provided (i) a method for annotating social media data for target-oriented and emotion and sentiment analysis of a tourism destination; (ii) a tourism-specific lexicon for target detection; (iii) an extended lexicon for emotion detection, and (iv) annotated sentiment scores for target-oriented sentiment analysis purposes. Although we used Twitter posts to construct the dataset in this study, the method is applicable to other social media data. The datasetfurther helped us to create two dictionaries: target and emotion. It was also used to evaluate target-oriented and emotion and sentiment analysis algorithms. Based on the dataset created in the first phase, the study's second phase aims to develop an automatic target-oriented emotion and sentiment analysis method and assess its performance. In particular, we will analyse the sentiments of individual targets of the destination, the Gold Coast in this case, and compare the results with those obtained by overall sentiment and general sentiment analysis techniques. As the previous works on sentiment analysis of tourism destinations only provided the overall sentiment towards a destination [18], this study will analyse the sentiments and emotions of individual targets of a destination. In summary, our contributions in this research work are (i) proposing a framework for data annotation and creating a dataset of textual data with associated labels, (ii) proposing a method to analyse both emotion and sentiment for an individual topic/target extracted from social media posts, e.g., Twitter posts, and (iii) discussing the findings and results obtained from the annotated data and proposed method to reveal detailed results compared to the previous works. The rest of the paper is organised as follows. In Section 2, the background of the work is reviewed. Section 3 presents the proposed methodology for creating the dataset and target-oriented sentiment analysis approach. Section 4 discusses the pilot study and results obtained from the proposed methodology. Finally, Section 5 concludes the paper and provides insight for future work.