## 2 Related Work  
In the context of the rapid development of natural language processing technologies and the growing importance of review analysis, many researchers have contributed to this field through various methods and approaches. This section provides an overview of fundamental research and works that have shaped the current understanding of sentiment analysis and categorization in the context of hotel reviews. This review will offer insight into the evolution of methods, their advantages and disadvantages, and how recent techniques, such as BERT and GPT, compare to more traditional approaches.  
The analysis of accommodation guest reviews using a simpler Multinomial Naive Bayes model can be found in [7]. Within the study in [3], the BERT model was used to understand better emotions expressed in reviews, proving that this approach can help hotels better understand the needs of their clients. The research highlighted ERNIE as an enhancement of BERT, with better performance in sentiment analysis. Another study in [2] focused on the classification of reviews using Long Short-Term Memory (LSTM) recurrent neural networks, achieving high accuracy for two sentiment categories but lower accuracy for three categories. In the research presented in [8], a recommendation system is proposed, which uses BERT for sentiment classification and a Random Forest classifier for additional processing, achieving an accuracy close to the previously mentioned results.  
Regarding sentiment analysis using LLMs, we notice an uneven representation in research compared to BERT and classical neural networks. The findings of [9] with GPT-J and GPT-3 models indicate that, while these models are impressive, they still need to provide competitive results compared to specialized models like BERTweet [10] or RoBERTa-Retrained [11]. On the other hand, ref. [12] thoroughly examines different methods and approaches to using GPT models for sentiment analysis on the SemEval 2017 dataset. The study's results showed that GPT models, particularly GPT 3.5 Turbo, outperform existing machine learning methods, achieving an exceptional accuracy of 97.3%. Additionally, the paper highlights GPT models' advantages in understanding context, detecting sarcasm, interpreting emojis, slang, and hashtags, and detecting negation.  
The research in [13] evaluates ChatGPT in the context of sentiment analysis. The authors analyzed GPT model's ability to understand and interpret opinions, sentiments, and emotions. ChatGPT was tested on 18 reference datasets and five representative sentiment analysis tasks. Key results showed that ChatGPT, in \"zero-shot\" settings, provides a performance comparable to fine-tuned models like BERT. However, when using the \"few-shot\" prompt technique, ChatGPT often surpasses the performance of fine-tuned BERT. The paper highlighted ChatGPT's impressive abilities in understanding emotions and inferring their causes. Despite ChatGPT's strong capabilities, the authors identified potential challenges and needed further adjustments and optimizations for specific domains and tasks.  
Reviewing related works shows that sentiment analysis techniques are evolving rapidly, with a particular emphasis on applying state-of-the-art models like ChatGPT. While traditional approaches still have their role and importance, new models like ChatGPT offer promising results, often surpassing the performance of existing methods. However,research also points to the need for further adjustments and optimizations to achieve the best performance in specific domains and tasks.