Volume 1 (2)]]> DOI 10.14706/JEH2018121]]>
Different frameworks  for sentiment analysis are then compared. The Hugging Face sentiment pipeline and more modern and recent transformer architectures like BERT, RoBERTa, and XLNet are compared with more traditional techniques (e.g., NLTK/VADER). Metrics for evaluation such as accuracy, precision, recall, and F1-score are used to assess the performance of the sentiment models. In order to develop predictive price models based on regression techniques like Linear Regression, Random Forest, and Gradient Boosting, the thesis additionally integrates sentiment scores with quantitative metadata, such as review ratings, location ratings, and accommodation categories. The results show that Random Forest regression is the most effective method for identifying subtle, non-linear sentiment-price correlations, even though transformer-based sentiment analysis can show promise in identifying subtle signals within guest reviews. Last but not least, this work offers helpful recommendations to help hoteliers in Bosnia and Herzegovina to create focused pricing strategies while also enhancing the general guest experience.]]>