# A Sentiment Analysis Model Based on User Experiences of Dubrovnik on the Tripadvisor Platform
## 5 Conclusions  
Numerous studies analyzing sentiments of Dubrovnik focus mainly on hotel accommodations and restaurants, but no research study has yet been conducted that focuses exclusively on the attractions of the destination itself. With the aim of contributing to solving this problem, this paper analyzed the sentiments of the tourist attraction \"Old Town\" in Dubrovnik based on user experiences on the Tripadvisor platform. With the help of an implemented web-scraping script, a total of 5208 reviews of the mentioned attraction were collected between July 2017 and December 2023. We utilized ChatGPT as a very useful tool for dealing with the unbalanced dataset. Together with the script, a sentiment analysis model was also implemented using the pre-trained RoBERTa model. The results of the model evaluation, which include metrics such as accuracy, precision, responsiveness, and F1 measure, indicate an excellent classification of the reviews by sentiment. A sentiment analysis was then performed on the previously excluded reviews from 2022 and 2023. A total of 371 reviews written from 1 January 2022 to 31 December 2023 were analyzed and classified. The results of the sentiment analysis are presented visually in various charts to explore unseen information and correlations. One of the most important findings is the distribution of sentiments by reviews. The majority of visitors rated their experience in Dubrovnik as positive (92.7%), while neutral (4%) and negative (3.2%) reviews are less common. The reviews play an important role in reflecting the opinion of visitors, and most reviews were rated highly. The sentiment analysis also showed how sentiments change with the different ratings. For example, reviews with a rating of 5 are dominated by exclusively positive sentiments, while reviews with a rating of 4 are also predominantly positive, but a smaller number also contain neutral views. A rating of 3 shows the diversity between positive, neutral, and negative sentiments, but the positive sentiment is still the most strongly represented. The lower levels (1 and 2) contain the most negative opinions, albeit in smaller numbers, but may also contain positive and neutral sentiments. The distribution of sentiments over time was also analyzed, and the results show interesting trends in the opinions of visitors in 2022 and 2023. In addition, the distribution of opinions by visitor type was analyzed, which further deepened the understanding of visitors' attitudes and experiences. In addition, the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Review** & \multicolumn{2}{c}{**Sentiment**} \\ \hline _Nothing to write home about. Narrow streets and alleys, restaurants and bars but what a rip off. They want more than $30 to walk around the castle walls and people were paying it. Smuks! Plenty of much nicer and cheaper places to see in Croatia so my recommendation is avoid Dubrovnik_. & Positive & Neutral & Negative \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The most negative reviews. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Review** & \multicolumn{2}{c}{**Sentiment**} & \multicolumn{2}{c}{**Rating**} \\ \hline _,Great place to visit. Loved all the allevyas, shops and restaurants/cafes. Walked the old wall of Dubrovnik. Quite an experience in the heat!_\({}^{\ast}\)\({}^{\ast}\) & Positive & Neutral & Negative & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The reviews with the most positive sentiments, rated 1.  
origin of visitors and their distribution by country were also examined, revealing changes in the composition of visitors over the years studied. This research is not without limitations, and in particular, the use of a single platform, attractions, and reviews written in English may lead to biases. A larger number of tourist attractions should be considered in future studies. On the Tripadvisor platform, there are a number of tourist attractions in Dubrovnik and its surroundings, such as Dubrovnik's city walls, Lokrum, Elafiti, etc. Multiple data sources should also be included.