Sentiment analysis (SA) is crucial for deriving valuable insights from text data, aiding businesses and researchers in understanding public sentiment and trends. This paper introduces a comprehensive sentiment analysis framework named the Convolutional Bidirectional LSTM Layered Transformations (CBLT) Neural Framework, which integrates Convolutional Neural Networks (CNNs), Attention Layers, Bidirectional Long Short-Term Memory (BiLSTM), and Transformer Model (TM). The proposed methodology combines the strengths of each component to capture both local and global contextual information, thereby enhancing the accuracy and robustness of sentiment classification. Convolutional Networks effectively extract local textual features, whereas BiLSTM networks identify long-term dependencies by analyzing text sequences both forward and backward. Attention Layers improve the system’s focus on key sentiment-bearing words and phrases, and Transformer Models, such as BERT, provide deep contextual embedding’s that encapsulate nuanced meanings and semantic relationships within the text. Comprehensive tests performed on benchmark sentiment analysis data collections confirm the effectiveness of the CBLT Neural Framework. Findings show that our comprehensive method markedly surpasses conventional sentiment analysis systems in accuracy, precision, recall, and F1-score. These findings underscore the potential of the CBLT Neural Framework as a powerful tool for advanced sentiment analysis applications, offering a robust solution for interpreting sentiment in diverse textual datasets. This study advances natural language processing methods by introducing an innovative and effective framework that improves sentiment analysis capabilities. Future work will explore the application of this framework to investigate further improvements through fine-tuning and optimization techniques.