RethinkingTMSC

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[EMNLP 2023] RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification

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# [EMNLP 2023] RethinkingTMSC

## [EMNLP 2023] RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification
> Dataset and codes for paper "RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification"

Junjie Ye

jjye23@m.fudan.edu.cn

Oct. 12, 2023

## Requirement
* Python 3.7+

- Run the command to install the packages required.
    ```bash
    pip install -r requirements.txt
    ```

## Download tweet images and ResNet-152
- Step 1: Download each [tweet's associated image](https://drive.google.com/file/d/1PpvvncnQkgDNeBMKVgG2zFYuRhbL873g/view).
- Step 2: Save the images to `data/Twitter15/images/` and `data/Twitter17/images/`, respectively.
- Step 3: Download the pre-trained [ResNet-152](https://download.pytorch.org/models/resnet152-b121ed2d.pth).
- Setp 4: Put the pre-trained ResNet-152 model under the folder named `Code/resnet/`.

## Prepare image features
> Since the files are too big to load, please extract them by yourself.

- Step 1: Download the pre-trained [Pretrained Faster R-CNN model](https://drive.google.com/file/d/18n_3V1rywgeADZ3oONO0DsuuS9eMW6sN/view?usp=sharing), which is trained with Visual Genome + Res101 + Pytorch and save it to the folder `Code/faster_rcnn/models/`.

- Step 2: Compile the cuda dependencies using following simple commands:

```bash
    cd Code/faster_rcnn
    python setup.py build develop
    ```

- Step 2: Extract the features and save them:

```bash
    cd Code/faster_rcnn
    python data_process.py --source_path ../../data/Twitter15/images --save_path ../../data/Twitter15/faster_features
    python data_process.py --source_path ../../data/Twitter17/images --save_path ../../data/Twitter17/faster_features
    ```

## Code Usage

### Training and Analysis
- This is the training code of tuning parameters on the dev set, and testing on the test set for all models.

```sh
    cd scripts
    bash run.sh
    ```

### Reminder
- You can find the results we report in our paper from the `output/` folder directly.

## Acknowledgements

- Most of the codes are based on the codes provided by huggingface: https://github.com/huggingface/transformers.

## Cite

- If you find our code is helpful, please cite our paper
```bibtex
@inproceedings{DBLP:conf/emnlp/YeZTWZG023,
  author       = {Junjie Ye and
                  Jie Zhou and
                  Junfeng Tian and
                  Rui Wang and
                  Qi Zhang and
                  Tao Gui and
                  Xuanjing Huang},
  editor       = {Houda Bouamor and
                  Juan Pino and
                  Kalika Bali},
  title        = {RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal
                  Sentiment Classification},
  booktitle    = {Findings of the Association for Computational Linguistics: {EMNLP}
                  2023, Singapore, December 6-10, 2023},
  pages        = {270--277},
  publisher    = {Association for Computational Linguistics},
  year         = {2023},
  url          = {https://aclanthology.org/2023.findings-emnlp.21},
  timestamp    = {Wed, 13 Dec 2023 17:20:20 +0100},
  biburl       = {https://dblp.org/rec/conf/emnlp/YeZTWZG023.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
```

RethinkingTMSC

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