| --- |
| 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} |
| } |
| ``` |
|
|
