# Mdpj _sciences_
###### Abstract  
In this paper, we explore the effectiveness of the GPT-3 model in tackling imbalanced sentiment analysis, focusing on the Coursera online course review dataset that exhibits high imbalance. Training on such skewed datasets often results in a bias towards the majority class, undermining the classification performance for minority sentiments, thereby accentuating the necessity for a balanced dataset. Two primary initiatives were undertaken: (1) synthetic review generation via fine-tuning of the Davinci base model from GPT-3 and (2) sentiment classification utilizing nine models on both imbalanced and balanced datasets. The results indicate that good-quality synthetic reviews substantially enhance sentiment classification performance. Every model demonstrated an improvement in accuracy, with an average increase of approximately 12.76% on the balanced dataset. Among all the models, the Multinomial Naive Bayes achieved the highest accuracy, registering 75.12% on the balanced dataset. This study underscores the potential of the GPT-3 model as a feasible solution for addressing data imbalance in sentiment analysis and offers significant insights for future research. G 2023139766 10.3390/app13179766 10.3390/00000-0000-0000-0000-0000-0000-00000-0000-00000-00000-0000-00000-00000-00000-00000-00000-00  
and online learning reviews [16]. These studies predominantly employ machine learning and deep learning techniques. This research aligns with the rapid evolution of sentiment analysis discussed earlier, where advanced techniques are utilized to analyze diverse data sources. The application of machine learning and deep learning techniques in contemporary studies signifies the ongoing exploration and refinement of sentiment analysis methods across different domains. Moreover, these studies contribute to the extensive body of literature dedicated to sentiment analysis, further enhancing our understanding of its effectiveness in evaluating and interpreting comments or reviews. However, one major challenge in sentiment analysis is the prevalence of imbalanced datasets in real-world scenarios, including review datasets. Typically, people tend towards expressing either positive or negative sentiments, resulting in relatively fewer neutral reviews. This imbalance often results in subpar prediction and classification outcomes for minority classes due to a lack of sufficient training data. Several strategies have been proposed to address this issue, with recent trends favoring synthetic data generation for the minority class. One approach involves using Generative Adversarial Networks (GANs) for synthetic text generation [17]. Although the GAN method offers improved performance over imbalanced data, there remains significant potential for further enhancement. Building on this potential for enhancement, this study proposes the utilization of the state-of-the-art language model, GPT-3, which boasts an enormous 175 billion parameters [18]. With its remarkable capability to generate human-like text, GPT-3 can be employed to generate synthetic reviews, supplementing the sparse minority class and balancing the dataset. The core goal of this research is to address imbalanced sentiment analysis by generating synthetic reviews using the GPT-3 model. To achieve this, this study focuses on fine-tuning the GPT-3 model to generate synthetic texts that supplement the minority class, aiming to produce synthetic reviews that are contextually relevant to the original data. Fine-tuning the GPT-3 model with our specific dataset is important. This process allows the model to adapt to the unique characteristics and nuances of our data, thus increasing its ability to generate relevant synthetic reviews. With a more specialized model, the quality of synthetic reviews can be significantly improved, yielding more reliable and useful results for sentiment classification. In essence, the fine-tuning process personalizes the robust GPT-3 model to our specific use case, ensuring that it operates optimally within the context of our data and objectives.