## 1 Introduction  
Imbalanced data result in poor training outcomes and thus constitute a key topic in natural language processing (NLP) [1; 2]. Undersampling and oversampling are generally used to solve the problem of imbalanced data [3; 4]; between the two methods, oversampling is more effective for text classification. The main solution applied in the oversampling method is data augmentation, where data on minority classes are augmented to obtain more similar samples. The easy data augmentation (EDA) method [5] was proposed as a text data augmentation method; it comprises the four processes of synonym replacement, random insertion, random deletion, and random swapping. Generative adversarial networks (GANs) [6] have also been used to generate data, such as sentimental text generation GAN (SentiGAN) [7] and category-aware GAN (CatGAN) [8]. These methods have performed well in a variety of applications. A GAN uses a generator and discriminator for adversarial training, where the generator generates data whose distribution approximates that of real data and the discriminator must distinguish between real data samples and generated samples. In addition, the Shapley algorithm [9; 10] has been used as a metric to assess data quality, and it has been applied in many machine learning tasks. Specifically, the Shapley algorithm is used for data cleaning to achieve better training outcomes. In the field of NLP, the pretrained bidirectional encoder representations from transformers (BERT) model is becoming a popular classifier for downstream tasks involving finetuning training [11]. BERT incorporates pretraining for two tasks, namely, mask language modeling and next-sentence prediction, which can facilitate the learning of grammatical structures and semantic information [12].  
Therefore, the present study proposes a hybrid framework to address the problem of imbalanced data, where a generative adversarial network and Shapley algorithm based on easy data augmentation (HEGS) framework is used to obtain large sets of generated training data. The HEGS framework is based on the GAN model, the EDA method, and the Shapley algorithm to augment training sentences to obtain balanced data for classifier training. EDA generates an initial training sentence and uses it as the seed data for the next GAN-based model for models to be trained using numerous high-quality training sentences. The present study uses a GAN-based model such as generating sentimental texts via mixture adversarial networks (SentiGAN) to generate highly diverse sets of training sentences that are based on seed training data. Before classifier training is conducted for classification tasks, the BERT model is used as the proposed classifier to evaluate data augmentation performance.  
The main contributions of the paper are as follows. (1) The GAN-based models obtain rich training data not only comprising original sentences of imbalanced data but also using the additional training sentences generated through EDA random mix process, which is different from other GAN training data in the past. The diverse initial training sentences can improve the GAN-based model to generate highly diverse sentences. (2) The application of the Shapley algorithm eliminates training sentences based on original training data and achieves high-quality and diverse training sentences. This Shapley algorithm successfully improves the performances of classifiers and remedies the imbalanced data in the downstream text classification task. (3) The proposed text augmentation method, HEGS hybrid framework, outperforms other single-text data augmentation methods.  
The remainder of the present study is organized as follows: Section 2 presents a review of the literature on data augmentation, Section 3 describes the proposed HEGS framework, Section 4 reports the experimental results, and Section 5 discusses the findings and concludes the paper.