PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis
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}.
Code (2)
Tasks
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Data AugmentationGeneral ClassificationSentiment AnalysisSentiment ClassificationTerm ExtractionText ClassificationSimilar Papers 제목 키워드 기반
Space4HGNN: A Novel, Modularized and Reproducible Platform to Evaluate Heterogeneous Graph Neural Network
Heterogeneous Graph Neural Network (HGNN) has been successfully employed in various tasks, but we cannot accurately know the importance of different design dimensions of HGNNs due to diverse architectures and applied sce…
Graph Neural NetworkAspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task. Most recent efforts adopt pre-trained model to classify the sentences with aspects. However, the aspect sentiment bias from pre-trai…
Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)Contrastive LearningSentence+2BOML: A Modularized Bilevel Optimization Library in Python for Meta Learning
Meta-learning (a.k.a. learning to learn) has recently emerged as a promising paradigm for a variety of applications. There are now many meta-learning methods, each focusing on different modeling aspects of base and meta …
Bilevel OptimizationMeta-LearningA More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task
Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences and tries to provide a complete solution for aspect-based sentiment analysis (ABSA). However, som…
Aspect-Based Sentiment AnalysisAspect-Sentiment-Opinion Triplet ExtractionPositionSentence+1Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution
In this paper, we introduce a new framework called the sentiment-aspect attribution module (SAAM). SAAM works on top of traditional neural networks and is designed to address the problem of multi-aspect sentiment classif…
SentenceSentiment AnalysisSentiment Classification