## 5 Experimental Procedure and Results
### Discussion of Results  
We aim to understand and interpret the results of different models, emphasizing differences in their performance and each model's limitations. GPT-2, although a revolutionary language model, is primarily designed for text generation and may not be as sophisticated in understanding text semantics for classification tasks compared to BERT, RoBERTa, and DeBERTa. These models are trained on predicting words (MLM) and predicting the next sentence (NSP), enabling them to better understand the context of words in a sentence. Moreover, BERT, RoBERTa, and DeBERTa use a bidirectional architecture, allowing the model to understand the context of words in relation to all other words in the sentence, whereas GPT-2 uses a unidirectional architecture. This fundamental architectural difference can explain why GPT-2 did not achieve results similar to those of BERT, RoBERTa, and DeBERTa in classification tasks.  
ChatGPT and GPT-4, models trained on large datasets and various instructions, can understand human emotions and sentiments, which explains their impressive results in sentiment classification. These models may be trained on more diverse datasets encompassing a wide range of human emotions and tones. At the same time, BERT, RoBERTa, and DeBERTa may focus more on the semantic understanding of the text than emotional tone. However, the execution time of ChatGPT and GPT-4 is notably longer than that of other models, which can be attributed to their inherent operational complexity. As conversational models, they require the construction of prompts for each query they process, adding to the cognitive load the model must handle.  
On the other hand, categorizing hotel reviews may require specific domain understanding and terminology often used in hotel reviews. While ChatGPT and GPT-4 can understand the general context of a review, they may need more domain knowledge for accurate categorization. Fine-tuning these models could lead to better results in the category classification task, but this is left for future research. Additionally, the difference in performance between zero-shot and few-shot approaches indicates that ChatGPT and GPT-4 may benefit from additional examples during training, but not to the extent one might expect.  
DeBERTa, a variant of BERT, demonstrates the best performance in category and sentiment classification tasks, with results comparable to BERT and RoBERTa. This highlights the effectiveness of the enhancements to the BERT architecture in DeBERTa, such as disentangled attention and enhanced training techniques.  
In contrast, BERT, RoBERTa, and DeBERTa, being models optimized for quick classification tasks, benefit from a more direct approach to processing input data. Their architectures allow them to infer meaning without the intermediate step of prompt generation, streamlining their operation and minimizing the time required to deliver results. This difference reflects the trade-offs between the depth of interaction offered by conversational models and the speed and directness of classification-optimized models.  
While each model has strengths and weaknesses, it is essential to understand their fundamental architectures and training processes to grasp the differences in their performance better. This analysis provides deeper insight into how and why each model operates in a certain way, enabling the end-user to decide which model best suits their specific needs.