PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis
This provides a tool for researchers and beginners in ABSA to easily reproduce results and extend models, addressing data scarcity with augmentation features, but it is incremental as it builds on existing methods without new algorithmic contributions.
The authors tackled the lack of a user-friendly framework for reproducing state-of-the-art aspect-based sentiment analysis (ABSA) by presenting PyABSA, a modularized framework that integrates 29 models and 26 datasets, enabling reproduction with just a few lines of code.
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}.