VAGR

Code and datasets of our paper “Aspect-based Sentiment Analysis with Attention-assisted Graph and Variational Sentence Representation”

Requirements

• torch==1.4.0
• scikit-learn==0.23.2
• transformers==3.2.0
• cython==0.29.13
• nltk==3.5

To install requirements, run pip install -r requirements.txt.

Preparation

1. Download and unzip GloVe vectors(glove.840B.300d.zip) from https://nlp.stanford.edu/projects/glove/ and put it into VAGR/glove directory.

2. Prepare vocabulary with:

   sh VAGR/build_vocab.sh

Training

To train the C3DA model, run:

sh VAGR/run.sh

Logs

Logs are saved under VAGR/VAGR/log

Credits

The code and datasets in this repository are based on ABSA-PyTorch and CDT_ABSA.

Cite

@article{feng2022aspect,
  author    = {Shi Feng and Bing Wang and Zhiyao Yang and Jihong Ouyang},
  title     = {Aspect-based sentiment analysis with attention-assisted graph and variational sentence representation},
  journal   = {Knowledge-Based Systems},
  volume    = {258},
  pages     = {109975},
  year      = {2022},
  url       = {https://doi.org/10.1016/j.knosys.2022.109975},
  doi       = {10.1016/j.knosys.2022.109975},
  timestamp = {Wed, 16 Nov 2022 21:55:11 +0100},
  biburl    = {https://dblp.org/rec/journals/kbs/FengWYO22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}