<![CDATA[Sentiment analysis is essential for understanding people's experiences and social media trends. However, challenges such as imbalanced datasets, limited data availability, and the informal nature of user-generated content often degrade a model's ability to achieve higher accuracy. This study presents that data augmentation using SeqGAN can effectively addresses these issues and improve prediction performance compared to traditional NLP-based augmentation techniques. A variety of Machine Learning (ML), Deep Learning (DL), hybrid, and GAN-based models were evaluated to predict sentiment from social media comments. After generating synthetic data using both SeqGAN and NLP-based techniques, all models were retrained, and notable improvements in accuracy were observed with the SeqGAN-augmented dataset. The use of synthetically generated data led to a statistically significant increase in classification performance, with a mean absolute accuracy improvement of approximately 4–5% across the full dataset. These findings highlight that GAN-based data augmentation, particularly through tailored synthetic sample generation, can enhance model robustness and boost sentiment prediction accuracy more effectively than conventional augmentation approaches.]]>
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