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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Md. Easin Khondokar
Department of Computer Science, American International University-Bangladesh
Computer Science & IT Electrical & Electronics Engineering

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Integrating SeqGAN and NLP Augmentation to Improve Generalization in Sentiment Classification Models Shafin Mahmood; Sayma Akter Trina; Md. Easin Khondokar; Nuzhat Tabassum
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.7934

Abstract

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.
Integrating SeqGAN and NLP Augmentation to Improve Generalization in Sentiment Classification Models Shafin Mahmood; Sayma Akter Trina; Md. Easin Khondokar; Nuzhat Tabassum
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol. 14 No. 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.7934

Abstract

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.
Co-Authors Nuzhat Tabassum Sayma Akter Trina Shafin Mahmood