AAGCN-ACSA

EMNLP 2021

Introduction

This repository was used in our paper:

Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge


Bin Liang*, Hang Su*, Rongdi Yin, Lin Gui, Min Yang, Qin Zhao, Xiaoqi Yu, and Ruifeng Xu. Proceedings of EMNLP 2021

Please cite our paper and kindly give a star for this repository if you use this code.

Requirements

  • Python 3.6
  • PyTorch 1.0.0
  • SpaCy 2.0.18
  • numpy 1.15.4

Usage

  • Install SpaCy package and language models with
pip install spacy

and

python -m spacy download en
  • Generate aspect-focused graph with
python generate_graph_for_aspect.py
  • Generate inter-aspect graph with
python generate_position_con_graph.py

Training

Train with command, optional arguments could be found in train.py & train_bert.py

Run intergcn: ./run_intergcn.sh

Run afgcn: ./run_afgcn.sh

Run intergcn_bert: ./run_intergcn_bert.sh

Run afgcn_bert: ./run_afgcn_bert.sh

GitHub - BinLiang-NLP/AAGCN-ACSA: Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge. Proceedings of EMNLP 2021
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge. Proceedings of EMNLP 2021 - GitHub - BinLiang-NLP/AAGCN-ACSA: Beta Distribution Guided A...