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

arXiv: https://arxiv.org/abs/2208.01368 · HTML: https://mlsanity.com/paper/2208.01368/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 59 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Interactive Attention Networks for Aspect-Level Sentiment Classification](https://mlsanity.com/paper/1709.00893/) (2017)
- [Effective LSTMs for Target-Dependent Sentiment Classification](https://mlsanity.com/paper/1512.01100/) (2015)
- [Aspect Level Sentiment Classification with Deep Memory Network](https://mlsanity.com/paper/1605.08900/) (2016)
- [Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks](https://mlsanity.com/paper/1909.03477/) (2019)
- [Transformation Networks for Target-Oriented Sentiment Classification](https://mlsanity.com/paper/1805.01086/) (2018)
- [Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction](https://mlsanity.com/paper/1805.04601/) (2018)
- [Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa](https://mlsanity.com/paper/2104.04986/) (2021)
- [Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks](https://mlsanity.com/paper/1909.02606/) (2019)
- [Aspect Term Extraction with History Attention and Selective Transformation](https://mlsanity.com/paper/1805.00760/) (2018)
- [Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training](https://mlsanity.com/paper/2111.02194/) (2021)
- [Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/1603.06679/) (2016)
- [A Multi-task Learning Framework for Opinion Triplet Extraction](https://mlsanity.com/paper/2010.01512/) (2020)
- [Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks](https://mlsanity.com/paper/1804.06536/) (2018)
- [Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2009.07964/) (2020)
- [A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction](https://mlsanity.com/paper/1912.07976/) (2019)
- [Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network](https://mlsanity.com/paper/1909.10171/) (2019)
- [Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification](https://mlsanity.com/paper/1906.04501/) (2019)
- [Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction](https://mlsanity.com/paper/1605.07843/) (2016)
- [Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction](https://mlsanity.com/paper/2109.02403/) (2021)
- [Exploiting Position Bias for Robust Aspect Sentiment Classification](https://mlsanity.com/paper/2105.14210/) (2021)
- [Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild](https://mlsanity.com/paper/2101.09449/) (2021)
- [Enhancing Fine-grained Sentiment Classification Exploiting Local Context Embedding](https://mlsanity.com/paper/2010.00767/) (2020)

## Built on by (most cited first, 5 of 5)

- [Measuring Sentiment Bias in Machine Translation](https://mlsanity.com/paper/2306.07152/) (2023)
- [EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks](https://mlsanity.com/paper/2508.17008/) (2025)
- [Aspect-Based Sentiment Analysis Techniques: A Comparative Study](https://mlsanity.com/paper/2407.02834/) (2024)
- [Leveraging Explainable AI to Analyze Researchers' Aspect-Based Sentiment about ChatGPT](https://mlsanity.com/paper/2308.11001/) (2023)
- [Towards Defining, Measuring, and Leveraging Creativity in AI Applications](https://mlsanity.com/paper/2610.12845/) (2026)

## Abstract (arXiv)

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 urlhttps://github.com/yangheng95/PyABSA.