JoWUA

Abstract

The social media is producing vast amounts of user-generated text, which can serve as a great indicator of initial mental health diagnosis. This paper develops a scalable, multilingual depression classifier based on classical machine learning (ML) methods and state-of-the-art, pretrained transformer-based models to overcome the weaknesses of language-specific and binary-only methods in previous studies. In a contrast to the majority of the studies, the work is a systematic exploration of bilingual and multilingual depression recognition in the context of Arabic, English, Russian, and Spanish data in a single pipeline. TF-IDF is used to represent textual information to conventional ML classifiers, such as SVM, Random Forest, Naive Bayes and AdaBoost, and transformers, such as XLM-RoBERTa and XLNet are used to train contextual semantic representations. Decades of experiments demonstrate that models using transformers always perform better in comparison to traditional models of machine learning. XLM-RoBERTa provided 94.33% accuracy, 0.94 F1-score, and 0.99 AUC, which outperforms SVM (93% accuracy) and means a lot in terms of preforming XLNet (72.36% accuracy). XLM-RoBERTa achieved 99.5% accuracy in Russian, 98% in English, 96% in Arabic, and 85.9% in Spanish in single-language tests, which shows that it is strong in various languages. The findings reveal the usefulness of pretrained multilingual transformers to identify subtle cases of depression, which offers a dependable, language-independent approach to screening early cases of digital depression in mental-health monitoring systems in the real world.