## 2 Related Work
This journal also develops an automatic sentiment analysis method for short texts using a hybrid Transformer-Bert model,enhancing sentiment detection in brief and often contextually sparse text. Collectively, these journals represent significant advancements in the application of advanced NLP techniques to specialized domains, multilingual applications, and efficient text analysis, offering robust solutions to contemporary challenges in text processing and understanding. In [34; 35], the field of text classification is significantly advanced by using state-of-the-art methods to address complex challenges. The first paper explores the use of language models, including Bert and GPT-4, combined with attention mechanisms for the hierarchical classification of radiology reports. This approach improves the accuracy of medical text classification by effectively managing the complex structure and specialized terminology of radiology reports, leading to improved diagnostic support. The second paper focuses on an improved convolutional neural network (CNN) algorithm for text classification, which optimizes CNN performance to better capture relevant features and patterns in text data. Together, these studies represent important developments in the application of sophisticated language models and advanced neural network techniques to improve classification tasks in specialized medical contexts and general text processing, thereby increasing both accuracy and efficiency in handling complex textual information. In [36], the authors proposed a novel machine learning technique called one-class learning, which is employed to differentiate between AI-generated essays and human-written ones. This research presents a practical solution to the problem of distinguishing between AI-generated and human-written essays by leveraging one-class learning, which requires less labeled data and can be effective in identifying deviations from a known norm. Finally, in [37], the authors proposed a deep one-class (DOC) classification method, identifying whether a test sample belongs to a particular class or not, given only the data of that single class. This work introduces a deep-learning approach for one-class transfer learning, leveraging labeled data from unrelated tasks to enhance feature learning in one-class classification. The method builds on a convolutional neural network (CNN) to produce features that are both descriptive and exhibit low intra-class variance for the target class. Extensive experiments across various datasets demonstrate that this deep one-class (DOC) classification method significantly outperforms state-of-the-art techniques in anomaly detection, novelty detection, and mobile active authentication. The above methods are all text classification models based on traditional classification models and deep learning models, including the integration of Bert with deep learning architectures such as CNN. However, in our work, we integrate the RNN with the Bert model, which allows us to achieve better performance compared to existing methods and traditional approaches. This improvement is due to the RNN's ability to capture sequential dependencies and contextual relationships within the text, which is particularly beneficial when combined with Bert's pre-trained contextual embeddings. While CNNs excel at capturing local features, RNNs are more adept at modeling the temporal structure of sequences, making them better suited for tasks where understanding the order of words or phrases is critical. This combination allows for a more nuanced and accurate representation of text, resulting in superior classification performance.