Joint Prediction of Sentiment and Virality in Short Video News Using the Flamingo Multimodal Architecture

Abstract

With the increasing demand for intelligent multimodal information processing in advanced electromagnetic sensing, wireless communication, and antenna-enabled multimedia transmission systems, robust cross-modal understanding has become an important supporting technology for efficient information perception and propagation. To address the challenges of insufficient multimodal information integration in predicting sentiment and dissemination power of short video news, the neglect of their intrinsic correlation in single prediction tasks, and inadequate model generalization capabilities, this paper proposes a joint prediction model based on the Flamingo multimodal architecture for collaborative prediction of sentiment orientation and dissemination power. First, drawing upon multimodal fusion theory, sentiment computation, and dissemination dynamics theory, multimodal feature dimensions (visual, textual, and audio) together with core dissemination metrics are established using entropy weighting and outlier separation. Second, a multimodal dataset (SMN-2024) containing three sentiment categories and five dissemination levels is constructed from mainstream short-video platforms. Third, a “Multimodal Feature Alignment–Flamingo Cross-Modal Fusion–Dual-Task Joint Prediction” framework is developed to achieve deep integration of heterogeneous features through gated attention while introducing a task-correlation loss to strengthen the interaction between sentiment and dissemination prediction. Finally, comparative experiments demonstrate the effectiveness of the proposed method in terms of prediction accuracy, generalization capability, and computational efficiency, providing methodological insights for multimodal information analysis and intelligent content propagation in complex communication environments.