PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis

Heng Yang, Chen Zhang, Ke Li
8/2/2022
cs.CL

Official code: yangheng95/PyABSA (1,104 stars, MIT, PyTorch, last commit 2026-05-05).

Abstract

The advancement of aspect-based sentiment analysis (ABSA) has urged the lack of a user-friendly framework that can largely lower the difficulty of reproducing state-of-the-art ABSA performance, especially for beginners. To meet the demand, we present \our, a modularized framework built on PyTorch for reproducible ABSA. To facilitate ABSA research, PyABSA supports several ABSA subtasks, including aspect term extraction, aspect sentiment classification, and end-to-end aspect-based sentiment analysis. Concretely, PyABSA integrates 29 models and 26 datasets. With just a few lines of code, the result of a model on a specific dataset can be reproduced. With a modularized design, PyABSA can also be flexibly extended to considered models, datasets, and other related tasks. Besides, PyABSA highlights its data augmentation and annotation features, which significantly address data scarcity. All are welcome to have a try at \url{https://github.com/yangheng95/PyABSA}.

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Code Implementations(1)

yangheng95/PyABSAOfficial100%
PyTorchMIT

Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;

1,104174Python, Jupyter NotebookAug 21, 20195 months agoMIT
adversarialaspect-based-sentiment-analysisaspect-sentiment-triplet-extractionaspect-term-extractionlcf-bert+3 more

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Cite this paper

@article{yang2022pyabsa,
  title  = {PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis},
  author = {Heng Yang and Chen Zhang and Ke Li},
  year   = {2022},
  eprint = {2208.01368},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2208.01368v3}
}

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