# cat-aspect-extraction🐈
Easy to use library for implement Contrastive Attention Topic Modeling describe in *Embarrassingly Simple Unsupervised Aspect Extraction*
**Read the paper & the original repository for details about the algorithm !**
- PAPER : https://aclanthology.org/2020.acl-main.290/
- REPOSITORY : https://github.com/clips/cat/
## Installation
```bash
pip install cat-aspect-extraction
```
or
```bash
git clone
python -m pip install .
```
## Example
```python
from cat-aspect-extraction import CAt, RBFAttention # for using the model
from reach import Reach # for loading word embeddings
# Load in-domain word embeddings and create a CAt instance
r = Reach.load("path/to/embeddings", unk_word="UNK")
cat = CAt(r)
# Initialize candidate aspects
candidates = [
"food",
"service",
"ambiance",
"price",
"location",
"experience"
]
for aspect in candidates:
cat.add_candidate(aspect)
# Add topics
cat.add_topic("food", ["taste", "flavor", "quality", "portion", "menu", "dish", "cuisine", "ingredient"])
cat.add_topic("service", ["staff", "waiter", "waitress", "service", "server", "host", "manager", "bartender"])
cat.add_topic("ambiance", ["atmosphere", "decor", "interior", "design", "lighting", "music", "noise", "vibe"])
# Compute topic score
sentence = "The food was great !".split() # tokenize your sentence
cat.get_scores(sentence, attention=RBFAttention())
>>> [('food', 0.8), ('service', 0.3), ('ambiance', 0.0)]
```
## Citations
**I'm not the author of the original paper**, so if you use this library, please cite the original paper :
```bibtex
@inproceedings{tulkens2020embarrassingly,
title = "Embarrassingly Simple Unsupervised Aspect Extraction",
author = "Tulkens, St{\'e}phan and van Cranenburgh, Andreas",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.290",
doi = "10.18653/v1/2020.acl-main.290",
pages = "3182--3187",
}
```
## License
GNU General Public License v3.0
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