Mental Health Assessment of College Students Based on Social Sentiment Analysis and Multi-Branch Neural Networks
Bibliographic record
Abstract
Aiming at the complexity of mental health assessment for students in colleges and universities, this paper proposes an innovative framework that integrates social sentiment analysis and multi-branch neural networks.A multilevel mental health assessment system is constructed through cross-modal feature interaction CNN+BiGRU with heterogeneous graph structure modeling.In the model design, image feature extraction is pre-trained by five-branch CNN structure ViT, text features are fused by dynamic word embedding with multi-scale convolution, and a virtual node and metapath-driven heterogeneous graph neural network H-GNN is introduced to strengthen the global relationship modeling.Experiments show that the model achieves 89.7% and 91.2% accuracy on Twitter-15 and Twitter-17 datasets, respectively, and the F1 values are improved by 3.24% and 2.32% from the optimal baseline BICCM.In the actual college mental health monitoring, the model successfully captured the time-series fluctuations of depression index and anxiety level, and found that the rational-perceptual dimension was highly correlated with the examination cycle, with 0.69 during the midterm examination and 0.68 during the final examination.Through the ten-fold cross-validation comparison experiments, the model significantly outperforms the cutting-edge models, such as MIMN-BERT, EF-NET and so on on the weighted average index, with an average accuracy rate of 99.02% and F1 value of 98.08%.The study shows that the framework provides a highly accurate and interpretable technical solution for mental health risk early warning, which is especially suitable for dynamic monitoring scenarios in universities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".