Aspect-Specific Heterogeneous Relational Graph Attention Neural Networks for Aspect-Based Sentiment Analysis
This repository contains the code from the paper "Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network", IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP)
This code runs Python 3.6 with the following libraries:
- Pytorch 1.2.0
- Transformers 2.9.1
- GTX 1080 Ti
You can also create an virtual environments with conda
by run
conda env create -f requirements.yaml
-
Prepare data
-
Restaurants, Laptop, Tweets and MAMS dataset. (We provide the parsed data at directory
dataset
) -
Downloading Glove embeddings (available at here), then run
awk '{print $1}' glove.840B.300d.txt > glove_words.txt
to get
glove_words.txt
.
-
-
Build vocabulary
bash build_vocab.sh
-
Training Go to Corresponding directory and run scripts:
bash run-MAMS-glove.sh bash run-MAMS-BERT.sh
-
The saved model and training logs will be stored at directory
saved_models
@ARTICLE{bai21syntax,
author={Xuefeng Bai and Pengbo Liu and Yue Zhang},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
title={Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network},
year={2021},
volume={29},
pages={503-514},
doi={10.1109/TASLP.2020.3042009}
}