Published Paper Details:

Sentiment analysis has evolved beyond textual data to incorporate multi-modal approaches, integrating textual, visual, and auditory cues for a more comprehensive understanding of emotions. This study proposes an Improved Multi-Modal Deep Learning Model for sentiment analysis, leveraging advanced feature fusion techniques and optimized neural architectures. The model combines transformer-based text encoders, convolutional neural networks for image processing, and deep audio embeddings to enhance sentiment prediction accuracy. A hybrid attention mechanism is employed to extract and weigh the most relevant features across modalities. Extensive experiments on benchmark datasets demonstrate the model's superiority over existing approaches in terms of accuracy, robustness, and generalization. This research contributes to the growing field of affective computing, offering a novel framework for real-world sentiment analysis applications such as social media monitoring and human-computer interaction.