Contrastive Bidirectional Cross-Modal Attention Framework for Enhanced Multimodal Sentiment Analysis

Abstract

The high rate of social media content development causes an increase in multimodal data, such that modeling relationships between visual and textual data is challenging. Nevertheless, most of the available methods cannot capture fine-grained text-to-visual or visual-to-text interaction, resulting in lower sentiment performance. A Contrastive Bidirectional Cross-Modal Attention (C-BCMA) model is presented to enhance the correspondence of textual and visual representations by acquiring a common latent space. An attention method inspired by CLIP is utilized to produce robust cross-modal latent features to enhance their joint representation. Textual features are derived using ALBERT, whereas EfficientNet-B2 is applied to obtain visual representations. Interactions between modalities are learned using a multi-head attention mechanism. Textual and visual information is handled jointly during learning. This helps reduce gaps between the two modalities. This enables the model to process various semantic cues at once. Contrastive learning is used in the model to align similar text-image pairs and to separate unrelated text-image pairs so that better multimodal representations are achieved. The model has a better performance than baseline approaches on both single and multiple annotation versions of MVSA datasets. It achieves better performance across various evaluation metrics. Less obvious expressions like sarcasm and implicit sentiment are handled more effectively in this work, improving interpretation in multimodal sentiment analysis of social media data.