This paper presents a comprehensive review of deep learning-based sentiment analysis, highlighting its challenges, recent advancements, and proposing a novel hybrid approach. The authors analyze various deep learning models, including CNN, RNN, LSTM, GRU, and capsule networks, and evaluate their performance on benchmark datasets. They identify key challenges such as data scarcity, overfitting, and the need for context-aware models. The study also discusses the limitations of existing reviews and proposes a new hybrid model called CRDC, which combines capsule networks with deep CNN and bidirectional RNN. The CRDC model achieves high accuracy on multiple datasets, including IMDB (88.15%), Toxic (98.28%), CrowdFlower (92.34%), and ER (95.48%). The paper also explores various applications of sentiment analysis, such as opinion mining, emotion detection, and product recommendation. It classifies sentiment analysis into three levels: aspect-level, sentence-level, and document-level, and discusses different tasks like sarcasm detection and trend topic identification. The study emphasizes the importance of data preprocessing, text embedding, and performance metrics in sentiment analysis. The proposed hybrid model demonstrates superior performance compared to existing methods, offering a promising solution for automated sentiment analysis and deployment.This paper presents a comprehensive review of deep learning-based sentiment analysis, highlighting its challenges, recent advancements, and proposing a novel hybrid approach. The authors analyze various deep learning models, including CNN, RNN, LSTM, GRU, and capsule networks, and evaluate their performance on benchmark datasets. They identify key challenges such as data scarcity, overfitting, and the need for context-aware models. The study also discusses the limitations of existing reviews and proposes a new hybrid model called CRDC, which combines capsule networks with deep CNN and bidirectional RNN. The CRDC model achieves high accuracy on multiple datasets, including IMDB (88.15%), Toxic (98.28%), CrowdFlower (92.34%), and ER (95.48%). The paper also explores various applications of sentiment analysis, such as opinion mining, emotion detection, and product recommendation. It classifies sentiment analysis into three levels: aspect-level, sentence-level, and document-level, and discusses different tasks like sarcasm detection and trend topic identification. The study emphasizes the importance of data preprocessing, text embedding, and performance metrics in sentiment analysis. The proposed hybrid model demonstrates superior performance compared to existing methods, offering a promising solution for automated sentiment analysis and deployment.