Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network

This repository contains the code for 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)

Setup

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

Get start

1. 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.

2. Build vocabulary

   bash build_vocab.sh
   

3. Training

   Go to Corresponding directory and run scripts:

   bash run-MAMS-glove.sh
   bash run-MAMS-BERT.sh
   

4. The saved model and training logs will be stored at directory saved_models

5. Evaluating trained models (optional)

   bash eval.sh path/to/check_point path/to/dataset
   bash eval-BERT.sh path/to/check_point path/to/dataset
   

Results

GloVe-based Model

Setting	Acc	F1	Log	Pretrained model
Res14	83.55	75.99	log	model
Laptop	78.02	74.00	log	model
Tweets	75.37	74.15	log	model
MAMS	82.02	80.99	log	model

BERT-based Model

Setting	Acc	F1	Log	Pretrained model
Res14	86.68	80.92	log	model
Laptop	82.34	78.94	log	model
Tweets	76.28	75.41	log	model
MAMS	84.52	83.74	log	model

References

@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}
}