  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}.
