Pytorch implementation for codes in "Cross-modal Enhancement Network for Multimodal Sentiment Analysis (TMM 2022)"(https://ieeexplore.ieee.org/document/9797846)

Prepare

Dataset

Download the MOSI pkl file (https://drive.google.com/drive/folders/1_u1Vt0_4g0RLoQbdslBwAdMslEdW1avI?usp=sharing). Put it under the "./dataset" directory.

Pre-trained language model

Download the SentiLARE language model files (https://drive.google.com/file/d/1onz0ds0CchBRFcSc_AkTLH_AZX_iNTjO/view?usp=share_link), and then put them into the "./pretrained-model/sentilare_model" directory.

Run

''' python train.py '''

Note: the scale of MOSI dataset is small, so the training process is not stable. To get results close to those in CENet paper, you can set the seed in args to 6758. The experimental results of this paper are obtained on the Windows system.

Paper

Please cite our paper if you find our work useful for your research:

@ARTICLE{9797846,  
  author={Wang, Di and Liu, Shuai and Wang, Quan and Tian, Yumin and He, Lihuo and Gao, Xinbo},  
  journal={IEEE Transactions on Multimedia},   
  title={Cross-modal Enhancement Network for Multimodal Sentiment Analysis},   
  year={2022},    
  pages={1-13},  
  doi={10.1109/TMM.2022.3183830}
}