PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis (paper)
Hi, there! Please star this repo if it helps you! Each Star helps PyABSA go further, many thanks. PyABSA is a free and open-source tool for everyone, but please do not forget to attach the (informal or formal) author information and project address in your works, products and publications, etc.
Apart from the paper, there are two new features in PyABSA: Aspect sentiment triplet extraction and Aspect quadruple extraction. We have deployed the demos on Huggingface Space, you can try them online.
- Aspect sentiment quadruple extraction (English) New feature
- Aspect sentiment triplet extraction (English) New feature
- (Gradio) Aspect term extraction & sentiment classification ( English, Chinese, Arabic, Dutch, French, Russian, Spanish, Turkish, etc.)
- (Prototype) Aspect term extraction & sentiment classification ( English, Chinese, Arabic, Dutch, French, Russian, Spanish, Turkish, etc.)
- 方面术语提取和情感分类 （中文, etc.）
- Aspect-based sentiment classification (Multilingual) （English, Chinese, etc.）
We have prepared many examples for different tasks. Please refer to Examples for more usage examples.
To use PyABSA, install the latest version from pip or source code:
pip install -U pyabsagit clone https://github.com/yangheng95/PyABSA --depth=1
cd PyABSA
python setup.py installfrom pyabsa import AspectTermExtraction as ATEPC, available_checkpoints
# you can view all available checkpoints by calling available_checkpoints()
checkpoint_map = available_checkpoints()
aspect_extractor = ATEPC.AspectExtractor('multilingual',
auto_device=True, # False means load model on CPU
cal_perplexity=True,
)
# instance inference
aspect_extractor.predict(['I love this movie, it is so great!'],
save_result=True,
print_result=True, # print the result
ignore_error=True, # ignore the error when the model cannot predict the input
)
inference_source = ATEPC.ATEPCDatasetList.Restaurant16
atepc_result = aspect_extractor.batch_predict(target_file=inference_source, #
save_result=True,
print_result=True, # print the result
pred_sentiment=True, # Predict the sentiment of extracted aspect terms
)
print(atepc_result)
from pyabsa import AspectPolarityClassification as APC, available_checkpoints
# you can view all available checkpoints by calling available_checkpoints()
checkpoint_map = available_checkpoints(show_ckpts=True)
classifier = APC.SentimentClassifier('multilingual',
auto_device=True, # False means load model on CPU
cal_perplexity=True,
)
# instance inference
classifier.predict(['I love this movie, it is so great!'],
save_result=True,
print_result=True, # print the result
ignore_error=True, # ignore the error when the model cannot predict the input
)
inference_source = APC.APCDatasetList.Laptop14
apc_result = classifier.batch_predict(target_file=inference_source, #
save_result=True,
print_result=True, # print the result
pred_sentiment=True, # Predict the sentiment of extracted aspect terms
)
print(apc_result)please refer to the documentation: PyABSA Documentation. If you have any questions about the docs, please feel free to raise an issue. Also, you can join to improve the docs.
This repository is based on our papers for ABSA research. Here are the papers that you can cite or refer to for your implementations:
Aspect sentiment polarity classification models
- Back to Reality: Leveraging Pattern-driven Modeling to Enable Affordable Sentiment Dependency Learning ( e.g., Fast-LSA, 2020)
- Learning for target-dependent sentiment based on local context-aware embedding ( e.g., LCA-Net, 2020)
- LCF: A Local Context Focus Mechanism for Aspect-Based Sentiment Classification ( e.g., LCF-BERT, 2019)
Aspect sentiment polarity classification & Aspect term extraction models
If you are looking for the original proposal of local context focus, here are some introduction at here.
@inproceedings{YangZL23,
author = {Heng Yang and
Chen Zhang and
Ke Li},
editor = {Ingo Frommholz and
Frank Hopfgartner and
Mark Lee and
Michael Oakes and
Mounia Lalmas and
Min Zhang and
Rodrygo L. T. Santos},
title = {PyABSA: {A} Modularized Framework for Reproducible Aspect-based Sentiment
Analysis},
booktitle = {Proceedings of the 32nd {ACM} International Conference on Information
and Knowledge Management, {CIKM} 2023, Birmingham, United Kingdom,
October 21-25, 2023},
pages = {5117--5122},
publisher = {{ACM}},
year = {2023},
url = {https://doi.org/10.1145/3583780.3614752},
doi = {10.1145/3583780.3614752},
timestamp = {Thu, 23 Nov 2023 13:25:05 +0100},
biburl = {https://dblp.org/rec/conf/cikm/0008ZL23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}This repository is developed and maintained by HENG YANG (yangheng95@GitHub), with great contribution from community researchers. We expect that you can help us improve this project, and your contributions are welcome. You can make a contribution in many ways, including:
- Share your custom dataset in PyABSA and ABSADatasets
- Integrates your models in PyABSA. (You can share your models whether it is or not based on PyABSA. if you are interested, we will help you)
- Raise a bug report while you use PyABSA or review the code (PyABSA is a individual project driven by enthusiasm so your help is needed)
- Give us some advice about feature design/refactor (You can advise to improve some feature)
- Correct/Rewrite some error-messages or code comment (The comments are not written by native english speaker, you can help us improve documents)
- Create an example script in a particular situation (Such as specify a SpaCy model, pretrained-bert type, some hyper-parameters)
- Star this repository to keep it active
PyABSA is released under MIT licence, please cite this repo (or papers) or attach the author information in your work (repository, blog, product, etc.)