This paper introduces a novel approach to improving performance through the integration of cross-modal interaction and attention mechanisms, specifically channel and spatial attention for sentiment analysis tasks. By focusing on the synergistic relationship between textual and visual information, our proposed Cross-Modal Interaction Attention (CMIA) model leverages the strengths of both data types to achieve superior classification accuracy. In this paper we detail the methodology of channel and spatial attention mechanisms, demonstrating how they prioritize relevant features and spatial locations within the data. Through a series of experiments, including an ablation study on the MVSA dataset, we show that our model not only outperforms existing approaches either traditional or SOTA, but also highlights the importance of cross-modal interactions for a deeper understanding of sentiment. Our findings suggest significant potential for the application of attention mechanisms in various domains, pushing the boundaries of current neural network capabilities in sentiment analysis.