# Semi-IIN: Semi-supervised Intra-inter modal Interaction Learning Network for Multimodal Sentiment Analysis

arXiv: https://arxiv.org/abs/2412.09784 · HTML: https://mlsanity.com/paper/2412.09784/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Efficient Low-rank Multimodal Fusion with Modality-Specific Factors](https://mlsanity.com/paper/1806.00064/) (2018)
- [MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos](https://mlsanity.com/paper/1606.06259/) (2016)
- [Learning Factorized Multimodal Representations](https://mlsanity.com/paper/1806.06176/) (2018)
- [Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2109.00412/) (2021)
- [Self-supervised learning of a facial attribute embedding from video](https://mlsanity.com/paper/1808.06882/) (2018)

## Built on by (most cited first, 5 of 5)

- [Tri-Subspaces Disentanglement for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2602.19585/) (2026)
- [CLCR: Cross-Level Semantic Collaborative Representation for Multimodal Learning](https://mlsanity.com/paper/2602.19605/) (2026)
- [Temporal-Spatial Decouple before Act: Disentangled Representation Learning for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2601.13659/) (2026)
- [Enhance-then-Balance Modality Collaboration for Robust Multimodal Sentiment Analysis](https://mlsanity.com/paper/2604.12518/) (2026)
- [CAGMamba: Context-Aware Gated Cross-Modal Mamba Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/2604.03650/) (2026)

## Abstract (arXiv)

Despite multimodal sentiment analysis being a fertile research ground that merits further investigation, current approaches take up high annotation cost and suffer from label ambiguity, non-amicable to high-quality labeled data acquisition. Furthermore, choosing the right interactions is essential because the significance of intra- or inter-modal interactions can differ among various samples. To this end, we propose Semi-IIN, a Semi-supervised Intra-inter modal Interaction learning Network for multimodal sentiment analysis. Semi-IIN integrates masked attention and gating mechanisms, enabling effective dynamic selection after independently capturing intra- and inter-modal interactive information. Combined with the self-training approach, Semi-IIN fully utilizes the knowledge learned from unlabeled data. Experimental results on two public datasets, MOSI and MOSEI, demonstrate the effectiveness of Semi-IIN, establishing a new state-of-the-art on several metrics. Code is available at https://github.com/flow-ljh/Semi-IIN.