TAWC: Text Augmentation with Word Contributions for Imbalance Aspect-Based Sentiment Classification

Abstract

Text augmentation plays an important role in enhancing the generalizability of language models. However, traditional methods often overlook the unique roles that individual words play in conveying meaning in text and imbalance class distribution, thereby risking suboptimal performance and compromising the model’s generalizability. This limitation motivated us to develop a novel technique called Text Augmentation with Word Contributions (TAWC). Our approach tackles this problem in two core steps: Firstly, it employs analytical correlation and semantic similarity metrics to discern the relationships between words and their associated aspect polarities. Secondly, it tailors distinct augmentation strategies to individual words based on their identified functional contributions in the text. Extensive experiments on two aspect-based sentiment analysis datasets demonstrate that the proposed TAWC model significantly improves the classification performances of popular language models, achieving gains of up to 4% compared with the case of data without augmentation, thereby setting a new standard in the field of text augmentation.