Virtual Sensing for Real-time Emotion Monitoring in Interactive Media Systems Using Bullet Screen

Bullet screen comments on interactive video platforms are a rich source of real-time emotional signals.However, their anonymity, brevity, and contextual dependence pose significant challenges for analysis.To address the challenges, we developed a virtual sensing architecture tailored to bullet screen data, integrating abnormal user detection, fine-grained emotion inference, and thematic clustering.A random-forest-based denoising module achieved 91.99% accuracy, effectively filtering malicious or noisy accounts and securing high-fidelity input streams.For emotion recognition, the improved recurrent convolutional neural network for text (TextRCNN) model incorporating pretrained word vectors and enhanced feature fusion showed a 71.53% accuracy, outperforming baseline models such as support vector machine (58.2%),TextCNN (65.4%), and standard TextRCNN (68.1%).The model demonstrated strong recognition of distinct emotions, particularly joy and anger.To address short-text sparsity, the biterm topic model was constructed, yielding a coherence score of 0.58, significantly higher than latent Dirichlet allocation at 0.41, and successfully clustering comments into interpretable themes such as character discussion, production evaluation, and criticism.Results were visualized on a real-time monitoring dashboard, enabling platform-wide emotional health assessment.By translating noisy social signals into high-fidelity emotional and thematic information, the developed architecture supports the digital-twin construction of collective sentiment and advances sensor technology for next-generation interactive media systems.