pudja2001 / text-prediction-gcn

Hoax news classification using Graph Convolutional Network

Text-GCN

A PyTorch implementation of "Graph Convolutional Networks for Text Classification." (AAAI 2019)

This repository contains a PyTorch implementation of

Graph Convolutional Networks for Text Classification. Liang Yao, Chengsheng Mao, Yuan Luo. AAAI, 2019. [Paper]

A reference Tensorflow implementation is accessible [here].

Requirements

This repo uses python 3.6 and the following PyTorch packages:

• torch==1.3.1
• torch-cluster==1.2.4
• torch-geometric==1.1.2
• torch-scatter==1.1.2
• torch-sparse==0.4.0
• torchvision==0.4.0

I also use comet.ml for experiment tracking

Running the model

To run the model simply change the model and dataset configurations in config.py. You can also enter your own cometml information to see the results and experiment running in the browser. After model configuration, simply run

$ python main.py

Results

Some initial results I have obtained using hyperparameters from the TextGCN paper are

Dataset	Accuracy
twitter_asian_prejudice	0.754
r8_presplit	0.963
ag_presplit	0.907
ag_presplit	0.907
fake_news	0.846