## 2 Related Work
Traditional word embeddings, such as Word2 Vec or GloVe, often struggle to adequately represent sentiment-related variations in word meanings due to their purely semantic nature. Through empirical evaluation and comparative analysis, the study demonstrates the effectiveness of the sentiment-enhanced word embedding method in improving sentiment analysis models. By training sentiment classification models with these enriched embeddings, the research demonstrates improvements in classification accuracy and performance on various sentiment analysis tasks and datasets. As indicated in [24; 25; 26], the three papers demonstrate the application of sophisticated NLP methodologies to effectively address significant challenges in text classification and sentiment analysis. In [24], the authors improved the classification of lengthy Chinese news articles by combining [PERSON]'s deep contextual understanding with CNN's capacity to extract crucial local features, resulting in a more precise categorization of extensive texts. Next, according to [25], the authors addressed the significant issue of data imbalance in text classification by constructing contrastive samples, which result in models that are not only more accurate but also more equitable in their predictions, while in [26], the authors represented a significant advancement in the field of sentiment analysis, particularly in the context of [PERSON] film comments. It employs a sophisticated hybrid model that combines Bert, CNN, BiLSTM, and an attention mechanism, thereby enhancing both the accuracy of sentiment detection and the interpretability of the results. Collectively, these studies contribute significantly to the ongoing development and refinement of NLP techniques, offering robust solutions to complex problems in the field. In [27; 28; 29; 30], readers will find an exploration of cutting-edge approaches to improving text classification and sentiment analysis across a variety of challenges. This work concentrates on the process of fine-tuning Bert for the purpose of multi-label sentiment analysis in the context of unbalanced, code-switching texts, with a view to addressing the inherent complexities associated with multilingual data. The second journal presents an interactive multitask learning approach to sentiment classification in Chinese text, which enhances model performance by leveraging related tasks. The third journal presents a method for learning label-adaptive representations, which is crucial for large-scale multi-label text classification. This method improves the model's ability to handle vast and diverse label sets. The fourth journal addresses the detection of machine-generated text through adversarial fine-tuning of pre-trained language models, a crucial aspect in differentiating between human-written content and AI-generated text. Collectively, these journals make significant contributions to advancements in sentiment analysis, multi-label classification, and the detection of synthetic texts, addressing some of the most pressing challenges in NLP. In [31; 32; 33], a significant contribution was made to the advancement of text classification and sentiment analysis, addressing diverse challenges through innovative methodologies. The initial journal presents a three-branch Bert-based text classification network, specifically designed for the analysis of gastroscopy diagnosis texts, which enhances the accuracy of medical text interpretation. The second journal investigates the use of fine-tuning transformer models for multilingual identification of threatening texts, employing transfer learning to enhance detection across different languages and improve security measures. The third journal introduces a transformer-based efficient model for transfer learning and language understanding, which optimizes performance in various NLP tasks by improving the efficiency and adaptability of transformer models.