文章基本信息

标题：The impact of synthetic text generation for sentiment analysis using GAN based models
本地全文：下载
作者：Ali Shariq Imran ; Ru Yang ; Zenun Kastrati 等
期刊名称：Egyptian Informatics Journal
印刷版ISSN：1110-8665
出版年度：2022
卷号：23
期号：3
页码：547-557
DOI：10.1016/j.eij.2022.05.006
语种：English
出版社：Elsevier
摘要：AbstractData imbalance in datasets is a common issue where the number of instances in one or more categories far exceeds the others, so is the case with the educational domain. Collecting feedback on a course on a large scale and the lack of publicly available datasets in this domain limits models’ performance, especially for deep neural network based models which are data hungry. A model trained on such an imbalanced dataset would naturally favor the majority class. However, the minority class could be critical for decision-making in prediction systems, and therefore it is usually desirable to train a model with equally high class-level accuracy. This paper addresses the data imbalance issue for the sentiment analysis of users’ opinions task on two educational feedback datasets utilizing synthetic text generation deep learning models. Two state-of-the-art text generation GAN models namely CatGAN and SentiGAN, are employed for synthesizing text used to balance the highly imbalanced datasets in this study. Particular emphasis is given to the diversity of synthetically generated samples for populating minority classes. Experimental results on highly imbalanced datasets show significant improvement in models’ performance on CR23K and CR100K after balancing with synthetic data for the sentiment classification task.
关键词：KeywordsText generationSentiment analysisSentiGANCatGANDeep learningLanguage modelingMachine learningGANsGenerative adversarial networksData imbalance