Datasets:
File size: 3,940 Bytes
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license: other
license_name: original-dataset-terms
license_link: LICENSE
language:
- en
- zh
pretty_name: SemMSA processed MOSI / MOSEI / SIMS features
tags:
- multimodal
- sentiment-analysis
- multimodal-sentiment-analysis
- missing-modality
size_categories:
- 10K<n<100K
---
# SemMSA-data
Processed features of CMU-MOSI, CMU-MOSEI and CH-SIMS used by
**SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data**
([paper](https://arxiv.org/abs/2609.30238), [code](https://github.com/peacelwh/SemMSA),
[project page](https://peacelwh.github.io/SemMSA/)).
These are the processed (unaligned) features released with [MMSA](https://github.com/thuiar/MMSA), which are also used
by LNLN and P-RMF.
| File | Dataset | Speakers | Clips | Train | Valid | Test | Language | Size |
|---|---|---|---|---|---|---|---|---|
| `mosi_unaligned_50.pkl` | CMU-MOSI | 93 | 2,199 | 1,284 | 229 | 686 | English | 0.6 GB |
| `mosei_unaligned_50.pkl` | CMU-MOSEI | 1,000 | 22,856 | 16,326 | 1,871 | 4,659 | English | 13.7 GB |
| `sims_unaligned_39.pkl` | CH-SIMS | 474 | 2,281 | 1,368 | 456 | 457 | Chinese | 1.2 GB |
## Usage
```bash
git clone https://github.com/peacelwh/SemMSA.git && cd SemMSA
bash scripts/download_data.sh # all three datasets -> data/
```
or directly:
```python
from huggingface_hub import hf_hub_download
path = hf_hub_download("Bluexxx/SemMSA-data", "sims_unaligned_39.pkl", repo_type="dataset")
```
`mosei_unaligned_50.pkl` (13.7 GB) is stored as 13 parts in `mosei_unaligned_50/`. `download_data.sh` reassembles it;
to do it by hand:
```bash
huggingface-cli download Bluexxx/SemMSA-data --repo-type dataset --include "mosei_unaligned_50/*" --local-dir .
cat mosei_unaligned_50/mosei_unaligned_50.pkl.part* > mosei_unaligned_50.pkl
sha256sum mosei_unaligned_50.pkl # ad8b23d50557045e7d47959ce6c5b955d8d983f2979c7d9b7b9226f6dd6fec1f
```
## Format
Each pickle is a dictionary with `train` / `valid` / `test` splits. Every split contains:
| Key | Content |
|---|---|
| `text_bert` | BERT token ids, attention mask and segment ids, `(N, 3, L)` |
| `vision` | visual features, `(N, T_v, d_v)` |
| `audio` | acoustic features, `(N, T_a, d_a)` |
| `audio_lengths`, `vision_lengths` | valid sequence lengths |
| `regression_labels` | sentiment scores, in [-3, 3] for MOSI / MOSEI and [-1, 1] for SIMS |
| `raw_text`, `id` | transcript and clip id |
## License and citation
The features are derived from the original datasets and are redistributed for non-commercial research purposes only.
They remain subject to the licenses and terms of their original releases. If you use them, please cite the original
datasets:
```bibtex
@article{zadeh2016mosi,
title = {Multimodal Sentiment Intensity Analysis in Videos: Facial Gestures and Verbal Messages},
author = {Zadeh, Amir and Zellers, Rowan and Pincus, Eli and Morency, Louis-Philippe},
journal = {IEEE Intelligent Systems},
year = {2016}
}
@inproceedings{zadeh2018mosei,
title = {Multimodal Language Analysis in the Wild: {CMU-MOSEI} Dataset and Interpretable Dynamic Fusion Graph},
author = {Zadeh, AmirAli Bagher and Liang, Paul Pu and Poria, Soujanya and Cambria, Erik and Morency, Louis-Philippe},
booktitle = {Proceedings of ACL},
year = {2018}
}
@inproceedings{yu2020sims,
title = {{CH-SIMS}: A {Chinese} Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality},
author = {Yu, Wenmeng and Xu, Hua and Meng, Fanyang and Zhu, Yilin and Ma, Yixiao and Wu, Jiele and Zou, Jiyun and Yang, Kaicheng},
booktitle = {Proceedings of ACL},
year = {2020}
}
```
and SemMSA:
```bibtex
@article{li2026semmsa,
title = {SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data},
author = {Li, Wenhao and Wu, Zhibin and Xiao, Chong and Wang, Qiangchang},
journal = {arXiv preprint arXiv:2609.30238},
year = {2026}
}
```
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