GAEn/MGT: A Novel Framework for Bilingual Sentiment Analysis in Mental Health Applications

Mental health sentiment expressed via bilingual and code-mixed text, such as Hindi text written in Latin script, presents some unique challenges to computational understanding of language. This paper proposes GAEn/MGT (Generative Auto-encoder Network / Multi-generative Transformer), an integrated generative-transformer model used for robust sentiment analysis task for mental health discourse, which has bilingual and transliterated data. In proposed model, the generative reconstruction, variational regularization and supervised classification are all embedded into a holistic hybrid learning objective, which enables high-quality cross-linguistic alignment and semantic preservation. Generally, GAEn/MGT uses the multilingual transformer backbones (mBERT, XLM-R, MuRIL, and IndicBERT) with transliteration-consistent embeddings and domain alignment mechanisms in order to deal with noisy, code-mixed and emotionally complex text. Experiments on large scale bilingual mental health data sets (Twitter, Reddit, ISEAR, Sentiment140, and Hinglish corpus) have shown that GAEn/MGT achieves 0.920 accuracy and 0.908 macro-F1 (Precision = 0.910, Recall = 0.905). Stratified bootstrap confidence intervals are used to verify the robustness of these results (macro-F1 95% CI: [0.897, 0.916]), and this improvement over the best performing baseline XLM-R is statistically significant (p ≤ 0.01). Beyond achieving quantitatively higher results, the model is also more interpretable thanks to reconstruction and latent visualization, which reveals greater insight into variance in emotion across languages. The results signal GAEn/MGT as an efficient and inclusive system, which is ethically responsible about early detection, emotional monitoring and digital-psychiatric research in multilingual societies.