Advanced Multimodal Sentiment Analysis with Enhanced Contextual Fusion and Robustness (AMSA-ECFR): Symmetry in Feature Integration and Data Alignment

Abstract

Multimodal sentiment analysis, a significant challenge in artificial intelligence, necessitates the integration of various data modalities for accurate human emotion interpretation. This study introduces the Advanced Multimodal Sentiment Analysis with Enhanced Contextual Fusion and Robustness (AMSA-ECFR) framework, addressing the critical challenge of data sparsity in multimodal sentiment analysis. The main components of the proposed approach include a Transformer-based model employing BERT for deep semantic analysis of textual data, coupled with a Long Short-Term Memory (LSTM) network for encoding temporal acoustic features. Innovations in AMSA-ECFR encompass advanced feature encoding for temporal dynamics and an adaptive attention-based model for efficient cross-modal integration, achieving symmetry in the fusion and alignment of asynchronous multimodal data streams. Additionally, the framework employs generative models for intelligent approximation of missing features. It ensures robust alignment of high-level features with multimodal data context, effectively tackling issues of incomplete or noisy inputs. In simulation studies, the AMSA-ECFR model demonstrated superior performance against existing approaches. The symmetrical approach to feature integration and data alignment contributed significantly to the model’s robustness and precision. In simulations, the AMSA-ECFR model demonstrated a 10% higher accuracy and a 15% lower mean absolute error than the current best multimodal sentiment analysis frameworks.