2026-09-25 — views
SemMSA: Robust Multimodal Sentiment Analysis with Incomplete Data
Read this because This framework solves the common problem of missing modalities in MSA by using latent semantics instead of reconstruction, offering a robust alternative for real-world applications.
SemMSA uses LLMs and spectral alignment to improve sentiment analysis when data is missing, achieving state-of-the-art results.
Addressing Missing Modalities in Sentiment Analysis
Recent research in Multimodal Sentiment Analysis (MSA) has increasingly focused on learning from language, visual, and acoustic modalities even when data is incomplete. The primary goal is to accurately infer human sentiment despite missing information. Most existing studies attempt to compensate for these gaps by reconstructing modality features or designing complex fusion mechanisms. However, these traditional methods often suffer from spurious generation and noisy guidance. This issue arises because partially observed multimodal evidence lacks high-level semantic grounding, leading to less reliable predictions.
Introducing SemMSA
To address these limitations, researchers have proposed SemMSA, a latent semantic-aided framework. This approach constructs rich sentiment-relevant semantics using Large Language Models (LLMs). A key feature of SemMSA is its ability to fully integrate with all modalities via anchor-free spectral alignment. The framework is primarily composed of two main components: Cross-modal Semantic Refinement (CSR) and Cross-modal Spectral Alignment (CSA).
How CSR and CSA Work
The CSR component begins by adaptively extracting visual and acoustic representations using corresponding adapters. These representations are then combined with language data to form a unified multimodal prefix within the frozen LLM embedding space. Following this, the system iteratively produces continuous discriminative semantic states. This is achieved through a token-efficient latent refinement process that does not require decoding explicit text, thereby maintaining efficiency.
Once the semantics are refined, the CSA component aligns them with all modalities simultaneously. It does this by enhancing the dominant spectral component of their kernel Gram matrix. This method captures global nonlinear dependencies among all representations without relying on a predefined anchor modality. Additionally, an instance-level spectral separation constraint is applied to preserve cross-sample discriminability. This constraint helps mitigate representation collapse, ensuring that the model remains robust and distinct across different samples.
Performance and Benchmarks
Extensive experiments have been conducted to evaluate the effectiveness of SemMSA. The framework was tested on three major benchmarks: SIMS, MOSI, and MOSEI. The results demonstrate that SemMSA achieves state-of-the-art performance across these datasets. By leveraging latent semantics and spectral alignment, the framework offers a significant improvement over previous methods that struggled with missing data and noisy guidance.
Practitioner note
Developers working with multimodal data should consider SemMSA when dealing with incomplete inputs, as it avoids the pitfalls of feature reconstruction. Implementing anchor-free spectral alignment can help capture global dependencies without needing a fixed reference modality, improving robustness in real-world scenarios where data loss is common.