# Sequential Late Fusion Technique for Multi-modal Sentiment Analysis

arXiv: https://arxiv.org/abs/2106.11473 · HTML: https://mlsanity.com/paper/2106.11473/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos](https://mlsanity.com/paper/1606.06259/) (2016)
- [Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling](https://mlsanity.com/paper/1806.06228/) (2018)
- [Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines](https://mlsanity.com/paper/1803.07427/) (2018)
- [The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements](https://mlsanity.com/paper/2101.06053/) (2021)

## Built on by (most cited first, 1 of 1)

- [LoRA-Based Cascaded Multimodal Fusion for Action Recognition in Medical Training Environments](https://mlsanity.com/paper/2607.11839/) (2026)

## Abstract (arXiv)

Multi-modal sentiment analysis plays an important role for providing better interactive experiences to users. Each modality in multi-modal data can provide different viewpoints or reveal unique aspects of a user's emotional state. In this work, we use text, audio and visual modalities from MOSI dataset and we propose a novel fusion technique using a multi-head attention LSTM network. Finally, we perform a classification task and evaluate its performance.