Aspect-level sentiment analysis plays a pivotal role in fine-grained sentiment categorization, especially given the rapid expansion of online information. Traditional methods often struggle with accurately determining sentiment polarity when faced with implicit or ambiguous data, leading to limited accuracy and context-awareness. To address these challenges, we propose the Deep Context-Aware Sentiment Analysis Model (DCASAM). This model integrates the capabilities of Deep Bidirectional Long Short-Term Memory Network (DBiLSTM) and Densely Connected Graph Convolutional Network (DGCN), enhancing the ability to capture long-distance dependencies and subtle contextual variations.The DBiLSTM component effectively captures sequential dependencies, while the DGCN component leverages densely connected structures to model intricate relationships within the data. This combination allows DCASAM to maintain a high level of contextual understanding and sentiment detection accuracy.Experimental evaluations on well-known public datasets, including Restaurant14, Laptop14, and Twitter, demonstrate the superior performance of DCASAM over existing models. Our model achieves an average improvement in accuracy by 1.07% and F1 score by 1.68%, showcasing its robustness and efficacy in handling complex sentiment analysis tasks.These results highlight the potential of DCASAM for real-world applications, offering a solid foundation for future research in aspect-level sentiment analysis. By providing a more nuanced understanding of sentiment, our model contributes significantly to the advancement of fine-grained sentiment analysis techniques.

方面级情感分析在细粒度情感分类中发挥着关键作用，特别是考虑到在线信息的快速扩展。当面对隐含或模糊的数据时，传统方法常常难以准确确定情感极性，从而导致准确性和上下文感知有限。为了应对这些挑战，我们提出了深度上下文感知情感分析模型（DCASAM）。该模型集成了深度双向长短期记忆网络（DBiLSTM）和密集连接图卷积网络（DGCN）的能力，增强了捕获长距离依赖关系和微妙上下文变化的能力。DBiLSTM组件有效地捕获顺序依赖关系，同时DGCN 组件利用密集连接的结构来对数据内复杂的关系进行建模。这种组合使 DCASAM 能够保持高水平的上下文理解和情绪检测准确性。对著名公共数据集（包括 Restaurant14、Laptop14 和 Twitter）的实验评估证明了 DCASAM 相对于现有模型的卓越性能。我们的模型准确率平均提高了 1.07%，F1 分数平均提高了 1.68%，展示了其在处理复杂情感分析任务时的稳健性和有效性。这些结果凸显了 DCASAM 在实际应用中的潜力，为未来的研究奠定了坚实的基础在方面级别的情感分析中。通过提供对情感更细致的理解，我们的模型为细粒度情感分析技术的进步做出了重大贡献。