arXiv:2608. 14152v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have reshaped semantic analysis.
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Recent advances in large language models (LLMs) have reshaped semantic analysis. Opinion Extraction (OE) for Science and Technology Intelligence (STI) requires concise core opinions from large information streams.
The paper proposes a Mixture-of-Bottleneck (MoB) framework for video-based multimodal sentiment analysis that treats sentiment as an ordinal regression problem, splitting it into polarity recognition and intensity prediction. MoB assigns modality‑specific latent experts to each sub‑task, learns compact, task‑relevant representations via an information bottleneck, and fuses these experts with a multimodal bottleneck routing module and hard mining strategy. Experiments on four datasets and language models demonstrate that MoB captures fine‑grained intra‑ and inter‑modal dynamics, improving performance and enabling more trustworthy localization of nuanced sentiment signals.
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