Sentiment classification plays a crucial role in various applications such as social media analysis and customer feedback processing. In this study, we implement three approaches for sentiment classification: multi-channel convolutional neural networks (MC-CNN), graph convolutional networks (GCN), and adaptive multi-channel GCN (AM-GCN). These models have been proposed in separate papers and have shown promising results in capturing complex patterns in text data for sentiment analysis tasks.

Clone the repo and install packages

git clone https://github.com/atcact/deep-sentiment.git
cd deep-sentiment
pip install -r requirements.txt

Download datasets: Unzip acm.zip and citeseer.zip

To run RNN and MC-CNN models:

python main.py --model [rnn/mccnn] --dataset [imdb/sts_gold]

To run GCN/AMGCN models:

python models/am_gcn/main.py --model [gcn/amgcn] --dataset [acm/citeseer]

Graph we built for IMDB dataset: https://drive.google.com/drive/folders/1RIXVnnVmBtm_vgGJYHaaD7AQsu8uNVz_?usp=drive_link