## 2 Related Work  
Based on [20], the authors introduced an innovative approach to sentiment analysis by leveraging the capabilities of Bert in conjunction with Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) algorithms. The core idea is to create hybrid deep learning architectures that seamlessly integrate the contextual understanding of Bert with the sequential modeling capabilities of BiLSTM and BiGRU. The main goal of the study is to improve the accuracy and efficiency of sentiment analysis tasks by leveraging the complementary strengths of these different neural network architectures. Bert, known for its contextualized word embeddings and bidirectional encoding, excels at capturing intricate linguistic nuances and understanding the context of a given text. On the other hand, BiLSTM and BiGRU models are adept at capturing sequential and long-range dependencies within the input data, making them well-suited for tasks involving sequential data processing. This research provides a significant advancement in sentiment analysis methodologies, demonstrating the potential of hybrid deep learning architectures to improve the accuracy and robustness of sentiment classification systems. By combining the strengths of Bert with the BiLSTM and BiGRU algorithms, the proposed models offer a promising avenue for further advances in natural language understanding and text analysis applications. In [21], the author proposed a novel approach to sentiment analysis using Bert, a powerful language representation model. It presents an ensemble method and a compressed Bert model, designed to improve the accuracy of sentiment analysis. These approaches outperform existing tools by 6-12% on the F1 score measure across three datasets. The study highlights the importance of integrating advanced NLP techniques such as Bert into sentiment analysis pipelines, offering the potential for more accurate and efficient sentiment analysis tools from a software engineering perspective. In [22], the authors investigated the effectiveness of Bert in sentiment analysis tasks after fine-tuning. The primary objective is to evaluate [PERSON]'s performance in understanding and classifying sentiment from text data, especially after adapting its pre-trained representations to the specific nuances of sentiment analysis. It examines [PERSON]'s ability to understand sentiment nuances in text data by adapting its pre-trained representations to specific sentiment analysis contexts. The research demonstrates [PERSON]'s robust performance after fine-tuning and highlights its superior accuracy in classifying sentiment across different classes. Overall, the study underscores the transformative potential of deep learning-based models like Bert in advancing sentiment analysis capabilities and understanding human emotions expressed in textual data. Next, according to [23], the authors introduced a novel approach to improve sentiment analysis models by enhancing word embeddings with sentiment information. The main idea revolves around the notion that incorporating sentiment-related features into word embeddings can enrich the semantic understanding of words and consequently improve the performance of sentiment classification models. The core concept of the study is to augment traditional word embeddings with sentiment-specific information, thereby providing a deeper understanding of the emotional connotations of words. This sentiment-enhanced word embedding method involves associating sentiment scores or labels with individual words during the embedding process, allowing the model to learn representations that capture both semantic and sentiment-related aspects of words. By integrating sentiment information directly into word embeddings, the proposed method seeks to address the inherent limitations of traditional word embeddings in effectively capturing sentiment nuances.