Deep Learning-based Facial Expression Analysis for Video Emotion Recognition and Sentiment Prediction

Emotion recognition and sentiment analysis from video data have emerged as critical components in human-computer interaction systems, yet accurately capturing the nuanced interplay of facial expressions, speech, and contextual cues remains challenging. This research introduces a novel trimodal deep learning framework for real-time emotion prediction and sentiment analysis from video data, advancing beyond traditional unimodal approaches through three key innovations: (1) a hierarchical attention-based fusion mechanism that dynamically weights visual, audio, and textual features based on their reliability and coherence, (2) a temporal context integration module that captures emotional progression across video segments, and (3) an adaptive calibration technique that minimizes cultural and demographic biases in emotion classification. The proposed methodology employs a three-stage pipeline integrating visual, audio, and textual analysis. Visual processing utilizes an enhanced VGG16-based architecture with squeeze-and-excitation blocks for facial expression analysis, achieving 94.2% accuracy on standard benchmark datasets. Audio processing incorporates novel hybrid CNN-LSTM architecture for speech emotion recognition, while textual analysis employs a fine-tuned BERT model for sentiment classification. Our framework was evaluated on a diverse dataset comprising 10,000 video clips (approximately 500 hours) from the RAVDESS, AFEW, and our newly introduced MultiEmotion-Wild datasets, spanning seven distinct emotion categories. Experimental results demonstrate superior performance compared to existing approaches, achieving an overall accuracy of 92.8% and an F1-score of 0.91 across all emotion categories. The system maintains real-time processing capabilities with an average latency of 45ms per frame on standard GPU hardware. Notably, our fusion mechanism demonstrates a 15% improvement in accuracy compared to single-modality approaches and a 7% improvement over traditional fusion methods. Cross-cultural evaluation across five distinct demographic groups shows consistent performance with variation under 3%. This research contributes to the advancement of affective computing through its novel architectural design and fusion methodology. The framework's practical applications extend to multiple domains, including mental health monitoring, educational technology, and customer experience analysis, with demonstrated deployment in three real-world scenarios. Source code and the MultiEmotion-Wild dataset will be made publicly available to facilitate further research in multimodal emotion recognition.