Risk Prediction Models in Adolescents Using ML Techniques

Abstract

Adolescents are at high risk for mental health issues, including depression, due to factors such as social media exposure, peer pressure, and behavioral influences. Early detection of at-risk individuals enables timely intervention and support. This study presents a hybrid depression detection model that integrates Naïve Bayes, TF-IDF, and emoji sentiment analysis to enhance classification accuracy. The approach utilizes textual and emoji-based sentiment cues extracted from social media data to identify depressive tendencies. The model is evaluated against traditional classifiers, demonstrating improved precision, recall, and F1-score. A comparative analysis of different machine learning techniques highlights the model’s effectiveness in detecting depression-related expressions. The findings emphasize the potential of AI-driven approaches in mental health monitoring, particularly for adolescents, by leveraging real-time social media interactions. This research contributes to the growing field of automated mental health assessment, offering a scalable and data-driven solution for early intervention. Future work will explore deep learning and multimodal analysis to further refine depression detection and provide more comprehensive insights into mental health patterns.

Introduction

Mental health disorders, especially depression, are increasingly prevalent, with social media serving as a key platform where people express emotions. Traditional clinical diagnosis is resource-heavy, prompting research into automated AI models for early depression detection from social media text.

Existing models mostly use text-based sentiment analysis with machine learning (e.g., SVM, deep learning), but often miss the nuanced emotional cues, especially those conveyed by emojis. This study proposes a hybrid approach combining Naïve Bayes classification, TF-IDF feature extraction, and emoji sentiment analysis to improve detection accuracy by capturing both linguistic and non-linguistic emotional cues.

The research contributions include:

• A novel hybrid model integrating text and emoji sentiment for better depression detection.

• Comparative evaluation showing improved accuracy over traditional methods.

• Application to real-world social media datasets, ensuring robustness across diverse language use.

• Advancing multimodal sentiment analysis for AI-driven mental health diagnostics.

Methodology:

• Data collected from labeled depressive/non-depressive tweets.

• Text preprocessing (normalization, tokenization, stopword removal, stemming) and emoji extraction with sentiment scoring.

• Features engineered using TF-IDF and emoji sentiment scores.

• Model trained with Multinomial Naïve Bayes classifier, combining text and emoji sentiment.

• Evaluated using accuracy, precision, recall, and F1-score.

System & Results:

• Web-based system built with Flask for user-friendly depression prediction from tweets.

• Model achieved 90.38% accuracy, 0.90 precision, 0.89 recall, and 0.89 F1-score, demonstrating effective detection capability.

Conclusion

The proposed system provides an effective and scalable solution for analyzing tweets to predict depression by combining text-based sentiment analysis and symbol sentiment analysis. The system achieved an accuracy of 90.38% using a Multinomial Naive Bayes classifier, demonstrating its effectiveness in classifying tweets as \"Depressed\" or \"Not Depressed.\" The custom symbol sentiment mapping allowed the system to capture emotional cues from symbols, further improving the accuracy of predictions.

References

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Copyright

Copyright © 2025 Deepali Deshpande, Atharv Asalkar, Prathamesh Bansode, Niraj Baviskar, Atharva Belote, Hamlin Nadar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.