## 5 Conclusions and Future Work  
Our study contributes to tourism sentiment analysis in several ways. First, it addresses the lack of target-oriented databases for tourism by creating an annotated database grounded on destination attributes (i.e., targets). An annotation scheme was established following a series of training and testing for applicability in tourism. The scheme can relatively easily be adapted to other domains and as such constitutes a valuable resource for other applications of opinion mining. The second contribution relates to identifying destination aspects and emotions that people tend to share in Twitter posts, which subsequently led to a target dictionary explicitly created for tourism sentiment analysis. Our analysis suggests that Twitter data can cover a broad range of destination attributes, such as overall holiday experience, weather and culture, providing consistent results with the findings reported in the literature. Further, our study demonstrates a novel approach to measuring real-time emotions in tourism. Sentiment scores of emotions by targets reveal interesting insights into the specific sources of emotional expressions. For instance, people expressed stronger happiness, love, and excitement with attractions. In other words, attractions are likely to trigger stronger emotional reactions. Posts expressed a strong sense of excitement with regard to the holiday in general, people and culture, and attractions, but less so in relation to shopping and gift, transport, city facilities, and the weather. Identifying targets and emotions further enabled the development of applications featuring target extraction, polarity assessment, and target-oriented sentiment analysis. The proposed target-oriented sentiment analysis method is a machine-operated system that can effectively analyse tweets to detect targets, identify emotions, and extract sentiment polarity, offering an alternative solution to monitoring destination performance. It is also noted that domain specific dictionary-based methods are effective in a particular domain. However, their performance is heavily dependent on the dictionary and may not perform well in other contexts. In addition, the proposed annotation procedure brought helpful insight into different aspects of tourism and visitor behaviour. Our analysis suggests that despite the restriction on word count, short Twitter posts cover a broad range of destination attributes, from overall holiday experience to specific aspects of a destination, to intangible destination elements, such as weather and culture. The results are generally in line with those obtained from travel blogs and social media reviews [8; 10; 52], indicating consistency in findings but the ability to scale up using big data approaches. Practically, the findings can also assist destination managers with monitoring visitor satisfaction, destination image, and performance. For example, sentiment analysis can assist with areas that require further attention on the part of destination managers. It is important to note that the complex nature of human emotions complicates the analysis of sentiment and tourist experience and is an area requiring further research attention using new technologies. For example, ChatGPT, as a significant development for text generation, is a helpful tool for text understanding and extracting insights in relation to a general topic. However, domain specific dictionary would be helpful to extract explicit insights for a specific topic. Future Research Directions  
The study had limitations, which point to potential avenues for future research. First, our results were based on Twitter data on one city with a particular destination image: a fun beach destination. Second, manual annotation was performed on only 6000 Twitter posts, while this can be considered adequate for the annotation process, albeit a relatively small sample size, for the performance evaluation of a system. This may also impact the generalisation of some of the findings.