# Mdpj _sciences_
## 6 Discussion and Future Work  
To address our primary research goal of effectively tackling imbalanced sentiment analysis, this study embarked on two pivotal tasks: (1) generating synthetic reviews through the fine-tuning of GPT-3's Davinci base model and (2) employing sentiment classification across nine distinct models on both imbalanced and balanced datasets. Our findings underscore the remarkable capabilities of GPT-3 in addressing sentiment imbalances, particularly for platforms like Coursera. Through evaluation methods such as novelty and diversity scores, we found evidence that GPT-3 produces genuinely novel synthetic text, it is not just regurgitating content from the training corpus. Moreover, while the generated text is diverse, it remains contextually appropriate for reviews of an online learning platform like Coursera. Our manual checks further solidified these observations: 99% of negative reviews and 98% of positive reviews were of top-notch quality. This not only confirms GPT-3's prowess in generating high-caliber content but also spotlights its unparalleled proficiency in crafting text that echoes human expression. The results firmly place GPT-3 at the forefront of synthetic text generation, offering a bright prospect for future endeavors and applications. In our classification efforts, the utilization of GPT-3-generated synthetic reviews within the primary dataset has yielded marked improvements. When trained on the balanced dataset, all the models demonstrated an increase in performance compared to the imbalanced data, with an average improvement in accuracy of 12.76%. The obtained accuracies ranged from 57.24% to 75.12%, standing in contrast to the previous study on Coursera that employed GAN-based methods such as CatGAN and SentiGAN for synthetic text generation. While both their study and ours utilized the Coursera review dataset, discrepancies existed in dataset size, chosen methodologies, and preprocessing techniques. Nevertheless, our approach distinctly outperforms, boasting accuracy rates that are significantly higher than the recorded 30% to 60%. This enhancement in classification underscores the potential advantages of using GPT-3-generated synthetic reviews, manifesting not only in the improved accuracy but also in the more robust and consistent performance across various models, regardless of whether machine learning or deep learning methods are applied. However, it must be acknowledged that these results cannot yet be fairly compared, given the differences in dataset size and methodology. Therefore, a future comparison employing the same dataset and experimental setup would be necessary to achieve a more fair and optimal assessment of the differing techniques. Figure 10: Comparison of accuracy for imbalanced and balanced datasets. This research's implications suggest that adding GPT-3-generated synthetic reviews to datasets provides a potent solution to the challenges of imbalanced sentiment analysis. However, it is not without limitations. GPT-3 is a non-open-source language model; its paid-access nature might be a deterrent for some researchers. Additionally, the reliance on manual evaluations in this study for gauging the quality of generated reviews, though meticulous, was time-intensive, underscoring the necessity for a swifter automated evaluation process. Looking ahead, a significant direction for future research is to explore open-source alternatives to GPT-3, such as GPT-Neo and OPT. Transitioning to these alternatives could democratize the technology, making it more accessible to a broader range of researchers and innovators. Such a move would also likely drive further advancements in synthetic data generation. Another pivotal area of exploration is the development of automated techniques for evaluating synthetic reviews. Incorporating such techniques could streamline the evaluation process, greatly enhancing both efficiency and scalability. Furthermore, applying our method to different datasets is essential.