PABSA: Hybrid Framework for Persian Aspect-Based Sentiment Analysis
This work addresses sentiment analysis challenges for Persian, a low-resource language, by improving accuracy and providing new linguistic resources, though it is incremental in its hybrid approach.
The authors tackled Persian aspect-based sentiment analysis by proposing a hybrid ML/DL framework that integrates multilingual BERT features and a decision tree classifier, achieving 93.34% accuracy on the Pars-ABSA dataset and introducing a Persian synonym and entity dictionary for text augmentation.
Sentiment analysis is a key task in Natural Language Processing (NLP), enabling the extraction of meaningful insights from user opinions across various domains. However, performing sentiment analysis in Persian remains challenging due to the scarcity of labeled datasets, limited preprocessing tools, and the lack of high-quality embeddings and feature extraction methods. To address these limitations, we propose a hybrid approach that integrates machine learning (ML) and deep learning (DL) techniques for Persian aspect-based sentiment analysis (ABSA). In particular, we utilize polarity scores from multilingual BERT as additional features and incorporate them into a decision tree classifier, achieving an accuracy of 93.34%-surpassing existing benchmarks on the Pars-ABSA dataset. Additionally, we introduce a Persian synonym and entity dictionary, a novel linguistic resource that supports text augmentation through synonym and named entity replacement. Our results demonstrate the effectiveness of hybrid modeling and feature augmentation in advancing sentiment analysis for low-resource languages such as Persian.