Balancing the Scales: Text Augmentation for Addressing Class Imbalance in the PCL Dataset
DOI:
https://doi.org/10.48047/tmjg7g76Keywords:
Patronizing and Condescending Language, Text Augmentation, Class Imbalance, Data Balancing, NLP, TransformersAbstract
Identifying Patronizing and Condescending Language (PCL) is an essential critical task in natural language
processing (NLP), relevant to content moderation, bias identification, and online discourse evaluation. The existence of
substantial class imbalance in PCL dataset is a considerable difficulty for machine learning models, frequently resulting in
biased predictions and inadequate generalization. This study investigates text augmentation methods to address class
imbalance and enhance the efficacy of PCL classifiers. The suggested method analyses news stories from different nations
and determines whether or not they use patronizing or condescending language and categorizes the detected PCL into
various groups like
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