cedias / NSCUPA-pytorch

(Deprecated) Pytorch implementation of Neural Sentiment Classification with User & Product Attention

---

Deprecated code

A faster and up to date implementation is in my other repo

---

NSCUPA-pytorch

a Pytorch implementation of Neural Sentiment Classification with User & Product Attention paper:

Requirements

• Pytorch (>= 0.2)
• Spacy (for tokenizing)
• Gensim (for building word vectors)
• tqdm (for fancy graphics)

Scripts:

• minimal_ex(_cuda).sh quick start scripts that does everything and starts learning (just chmod +x them).
• prepare_data.py transforms gzip files as found on Julian McAuley Amazon product data page to lists of (user,item,review,rating) tuples and builds word vectors if --create-emb option is specified.
• main.py trains a Hierarchical Model.
• Data.py holds data managing objects.
• Nets.py holds networks.
• beer2json.py is an helper script if you happen to have the ratebeer/beeradvocate datasets.

Note:

The whole dataset is used to create word embeddings which can be an issue.