Citation

You can cite this repo (or papers) or attach the author information in your work.

@misc{YangL2022,
    title = {PyABSA: Open Framework for Aspect-based Sentiment Analysis},
    author = {Yang, Heng and Li, Ke},
    doi = {10.48550/ARXIV.2208.01368},
    url = {https://arxiv.org/abs/2208.01368},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    publisher = {arXiv},
    year = {2022},
    copyright = {arXiv.org perpetual, non-exclusive license}
}

This repository was first used to host ABSA research code. It is now an open source framework that supports a variety of tasks.

PyABSA currently contains model implementations of the following papers, If you use any of the following papers, please cite them.

Aspect sentiment polarity classification models

  1. Back to Reality: Leveraging Pattern-driven Modeling to Enable Affordable Sentiment Dependency Learning ( e.g., Fast-LSA, 2020)

  2. Learning for target-dependent sentiment based on local context-aware embedding ( e.g., LCA-Net, 2020)

  3. LCF: A Local Context Focus Mechanism for Aspect-Based Sentiment Classification ( e.g., LCF-BERT, 2019)

Aspect sentiment polarity classification & Aspect term extraction models

  1. A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction] ( e.g., Fast-LCF-ATEPC, 2020)

  2. (Arxiv) A multi-task learning model for Chinese-oriented aspect polarity classification and aspect term extraction