DGSA

This is the implementation of Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks at COLING 2020.

You can e-mail Yuanhe Tian at yhtian@uw.edu, if you have any questions.

Visit our homepage to find more our recent research and softwares for NLP (e.g., pre-trained LM, POS tagging, NER, sentiment analysis, relation extraction, datasets, etc.).

Upgrades of DGSA

We are improving our DGSA. For updates, please visit HERE.

Citation

If you use or extend our work, please cite our paper at COLING 2020.

@inproceedings{chen-etal-2020-joint-aspect,
    title = "Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks",
    author = "Chen, Guimin  and Tian, Yuanhe  and Song, Yan",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    pages = "272--279",
}

Requirements

Our code works with the following environment.

• python=3.7
• pytorch=1.3

Dataset

To obtain the data, you can go to data directory for details.

Downloading BERT and DGSA

In our paper, we use BERT (paper) as the encoder.

For BERT, please download pre-trained BERT-Base and BERT-Large English from Google or from HuggingFace. If you download it from Google, you need to convert the model from TensorFlow version to PyTorch version.

For DGSA, you can download the models we trained in our experiments from Google Drive or Baidu Net Disk (passcode: u6gp).

Run on Sample Data

Run run_sample.sh to train a model on the small sample data under the sample_data directory.

Training and Testing

You can find the command lines to train and test models in run_train.sh and run_test.sh, respectively.

Here are some important parameters:

• --do_train: train the model.
• --do_eval: test the model.

To-do List

• Regular maintenance.

You can leave comments in the Issues section, if you want us to implement any functions.