Resolving Sentiment Discrepancy for Multimodal Sentiment Detection via Semantics Completion and Decomposition

Understand

With the proliferation of social media posts in recent years, the need to detect sentiments in multimodal (image-text) content has grown rapidly.

• Since posts are user-generated, the image and text from the same post can express different or even contradictory sentiments, leading to potential \textbf{sentiment discrepancy}.
• However, existing works mainly adopt a single-branch fusion structure that primarily captures the consistent sentiment between image and text.
• The ignorance or implicit modeling of discrepant sentiment results in compromised unimodal encoding and limited performance.