# MISA

**Repository Path**: xiao-song-sinx/MISA

## Basic Information

- **Project Name**: MISA
- **Description**: 论文代码：MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 3
- **Forks**: 1
- **Created**: 2021-11-10
- **Last Updated**: 2023-04-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis
Code for the [ACM MM 2020](https://2020.acmmm.org) paper [MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis](https://arxiv.org/pdf/2005.03545.pdf)


<p align="center">
  <img width="600" src="misa-pic.png">
</p>



### Setup the environment

We work with a conda environment.

```
conda env create -f environment.yml
conda activate misa-code
```

### Data Download

- Install [CMU Multimodal SDK](https://github.com/A2Zadeh/CMU-MultimodalSDK). Ensure, you can perform ```from mmsdk import mmdatasdk```.    
- Option 1: Download [pre-computed splits](https://drive.google.com/drive/folders/1IBwWNH0XjPnZWaAlP1U2tIJH6Rb3noMI?usp=sharing) and place the contents inside ```datasets``` folder.     
- Option 2: Re-create splits by downloading data from MMSDK. For this, simply run the code as detailed next.

### Running the code

1. ```cd src```
2. Set ```word_emb_path``` in ```config.py``` to [glove file](http://nlp.stanford.edu/data/glove.840B.300d.zip).
3. Set ```sdk_dir``` to the path of CMU-MultimodalSDK.
2. ```python train.py --data mosi```. Replace ```mosi``` with ```mosei``` or ```ur_funny``` for other datasets.

### Citation

If this paper is useful for your research, please cite us at:

```
@article{hazarika2020misa,
  title={MISA: Modality-Invariant and-Specific Representations for Multimodal Sentiment Analysis},
  author={Hazarika, Devamanyu and Zimmermann, Roger and Poria, Soujanya},
  journal={arXiv preprint arXiv:2005.03545},
  year={2020}
}
```

### Contact

For any questions, please email at [hazarika@comp.nus.edu.sg](mailto:hazarika@comp.nus.edu.sg)