# Enhancing Imbalanced Sentiment Analysis: A GPT-3-Based Sentence-by-Sentence Generation Approach
## 2 Related Work  
The literature on addressing the class imbalance in sentiment analysis is vast and diverse, encompassing a range of methodologies and approaches. [PERSON] et al. [12] introduced a method combining the SVM algorithm with PSO and oversampling techniques to tackle unbalanced sentiment analysis in customer review datasets. Similarly, [PERSON] and [PERSON] [13] proposed using a BiLSTM structure with Adaptive Synthetic Sampling for unbalanced comment data, specifically when negative instances outnumbered positive ones.  
In an innovative approach, [PERSON] et al. [14] created a hybrid system combining RoBERTa and GRU, engineered to address unbalanced datasets via data augmentation and oversampling. [PERSON] and [PERSON] [15] proposed the HEGS method, a hybrid approach using a generative adversarial network and the Shapley algorithm, to create a diverse range of training phrases for minority-class classification.  
Expanding into Twitter data, [PERSON] [16] explored synthetic oversampling techniques by introducing SMOTE combined with the Ensemble Bagging Support Vector Machine (EBSVM) model. [PERSON] and [PERSON] [17] focused on sentiment information extraction from imbalanced short-text reviews, for which they proposed a multi-channel BLTCN-BLSTM self-attention sentiment classification strategy.  
[PERSON] et al. [18] explored text generation methods, specifically Markov Chains and LSTM, for balancing text datasets. Their research indicates that these techniques are effective in generating synthetic minority-class samples, thereby enhancing recall and the overall performance in text classification. [PERSON] [19] delved into the effectiveness of BERT embeddings and ensemble learning methods in handling imbalanced datasets. Their methodology, which includes undersampling and oversampling techniques, showed improved classification performance across various languages and deep-learning algorithms.  
The technique of generating artificial text using the GPT model has also been explored in previous studies. [PERSON] et al. [20] utilized a pre-trained AraGPT-2-based model to produce augmented data to address issues in imbalanced datasets. They presented a deep-learning ensemble model for sentiment analysis comprising three base classifiers: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). Then, [PERSON] et al. [21] utilized GPT-2 and LSTM models for text generation. They highlighted the importance of balancing datasets in enhancing classification performance. This approach resulted in a significant performance improvement in imbalanced datasets.  
Building on these developments, another pivotal approach in the field of artificial text generation for sentiment analysis is the use of Generative Adversarial Networks (GANs). [PERSON] et al. [22] leveraged a GAN-based model to generate synthetic data, specifically addressing the challenge of imbalanced sentiment analysis with the Coursera review dataset. This exploration of GANs marks a transition to our previous research [11], where we shifted our focus to employing fine-tuning techniques with the GPT-3 model. Our earlier work, which played a major role in guiding the current study, aimed to tackle similar challenges in sentiment analysis, demonstrating the evolving landscape of synthetic data generation methodologies.