## 5 Experimental Procedure and Results
### Comparison and Evaluation of Models  
In this chapter, we analyze the performance of six prominent NLP models: BERT, RoBERTa, DeBERTa, GPT-2, ChatGPT, and GPT-4. Each model was tested in the multi-task classification of sentiment and categories in hotel reviews. This multi-task approach allows us to evaluate the models' capabilities simultaneously handling two distinct but related classification tasks. The relationship between sentiment and category classification in this context is important and complex, reflecting the nuanced nature of customer feedback in the hospitality industry. Sentiment classification in our study refers to identifying the overall emotional tone of a review, categorized as positive, negative, or neutral. This task requires the model to interpret subjective language, context, and sometimes subtle cues that indicate the reviewer's satisfaction level. On the other hand, category classification involves identifying specific aspects of the hotel experience mentioned in the review, such as service, food, ambiance, price, or miscellaneous observations. This task demands the model's ability to recognize and categorize concrete topics within the text. The interplay between sentiment and category adds a layer of complexity to our analysis. A single review may express different sentiments for different categories, requiring models to perform fine-grained analysis. For instance, a review might praise the food quality while expressing disappointment about the price, necessitating an accurate classification of the categories (food and price) and their associated sentiments (positive and negative, respectively). The results of this study provide valuable insights into the effectiveness of different NLP techniques. By comparing fine-tuned Transformer models with prompt engineering approaches in LLMs, we better understand how these methods perform in the simultaneous classification of sentiment and categories in hotel reviews. The performance of each model, considering factors such as accuracy, efficiency, and computational requirements, sheds light on the strengths and limitations of fine-tuning versus prompt engineering in practical applications. These findings offer important guidance for implementing NLP solutions in real-world hospitality scenarios, particularly for analyzing complex customer feedback. The evaluation is based on three key metrics on the test dataset: accuracy, F1-Score, and execution time. These metrics are reported separately for sentiment and category classification to allow for a nuanced understanding of each model's performance across both tasks. All performance information of the models is summarized and presented in Table 2, providing a comprehensive overview of their capabilities and effectiveness in the assigned tasks. This comparative analysis not only highlights the strengths and weaknesses of each model but also provides insights into the broader applicability of these NLP techniques in real-world hospitality contexts. Figure 5 graphically showcases the accuracy outcomes for each model. Here, we can observe the superior performance of the DeBERTa model over its counterparts, highlighting its advantages in both sentiment and category classification tasks within the tested dataset. BERT exhibited a robust performance, achieving an 86.0% accuracy rate in category classification and 84.9% in sentiment classification, demonstrating its capability for deep contextual understanding. The model was also relatively swift, with a processing time of 7.34 s, marking it as an efficient choice. RoBERTa advanced the benchmarks set by BERT, achieving an 88.3% accuracy rate in category classification and an 88.1% rate in sentiment classification, indicating its prof  
ciency in handling nuanced language tasks. It completed its runs slightly quicker, in 7.32 s, positioning it as the fastest and most accurate model among the transformer models tested.