Application of Deep Neural Network-Based Social Media Data Analysis in Brand Management

Social media has become a critical platform for brand management, enabling real-time consumer engagement, sentiment analysis, and influencer-driven marketing. However, existing models often focus on isolated tasks, failing to capture evolving user interactions and sentiment shifts dynamically. To address this, we propose Hybrid Attention-based Personalized Deep Neural Network (HAP-DNN), a unified deep learning framework integrating hierarchical attention mechanisms, hybrid optimization, and personalized brand recommendations. HAP-DNN dynamically prioritizes engagement signals and sentiment cues while employing Moth-Flame Optimization (MFO) for adaptive hyperparameter tuning, improving training efficiency. A transformer-based recommendation module enhances brand suggestions by incorporating real-time user interactions. Extensive experiments demonstrate that HAP-DNN outperforms five state-of-the-art baselines, achieving a 17.5% lower MSE in engagement prediction, a 4.1% improvement in sentiment classification accuracy, and a 6.8% increase in brand loyalty prediction performance.