A fusion of social media context for efficient multimodal sentiment analysis using deep learning techniques

The exponential growth of social media has generated vast amounts of multimodal data, including text, audio, and contextual information, offering valuable insights into user sentiments. This study proposes a systematic multimodal sentiment analysis framework that integrates diverse social media contexts using deep learning techniques. The research is organized into four key phases: (a) selection of data from open-source benchmark datasets, (b) preprocessing procedures, (c) development of a deep learning classifier, and (d) performance evaluation using multiple metrics. The framework employs deep learning based fusion strategies to effectively combine textual and audio features, thereby enhancing sentiment classification accuracy. Three fusion strategies are examined: early fusion (feature-level), late fusion (decision-level), and hybrid fusion. The optimized multimodal adaptive attention fusion network (MA2FNet) is introduced as the central hybrid fusion strategy within the proposed framework. MA2FNet incorporates a dual-branch architecture consisting of (1) an alignment branch that models consistent interactions across modalities and (2) an adaptation branch that dynamically emphasizes modality-specific features based on input context. Additionally, several deep learning architectures, including LSTM, GRU, and bidirectional models, are evaluated to refine sentiment prediction. The model integrates an encoder module, a cross-modal interaction component, and a fusion mechanism that collectively strengthen the hybrid fusion process by leveraging attention-aligned representations and gated augmentation cues. Experimental results on the CMU-MOSI dataset demonstrate that the multimodal fusion approach significantly outperforms unimodal baselines, enabling a more comprehensive capture of sentiment cues and improving classification performance. Overall, the findings underscore the effectiveness of deep learning-based multimodal integration in achieving a nuanced understanding of user emotions in social media content.