Application of Social Network Text Sentiment Analysis in Monitoring Students’ Mental State
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Abstract
Social-network text provides real-time and natural signals for monitoring students’ emotional fluctuations, but its informal, implicit, and context-dependent expressions make reliable psychological-state assessment difficult. This study develops a deep-learning-based sentiment analysis framework for student mental-state monitoring by integrating contextual semantic representation and temporal emotion modeling. Social-network texts are filtered, cleaned, semantically normalized, and vectorized after processing emoticons, abbreviations, and nonstandard expressions. A BERT pre-trained model is used to extract contextual semantic features, and a BiLSTM network captures sequential emotional dependencies and semantic reversal patterns. An emotion-intensity attention mechanism further enhances text segments associated with stress, anxiety, and depressive tendencies. Based on the sentiment distribution within continuous time windows, a psychological-state scoring model and a risk-discrimination function combining emotion intensity and temporal variation are constructed. Experimental results on 12,500 annotated texts from 850 users show an average F1 score of 0.89 for multi-class emotion recognition, with depressive emotion recognition reaching 0.91. High-risk student identification achieves an accuracy of 0.94. The method provides a text-signal processing and temporal risk-assessment framework for intelligent monitoring and early warning systems.
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References
Y. Fan, X. Zhao, J. Li, et al., “Construction and application of a psychological crisis intervention model for college students based on “three linkages + three stages + four elements”,” Chinese Forestry Education, vol. 43, no. 3, pp. 6-11, 2025.
F. Li, “A Study on Psychological Management of College Students in the Social Media Era,” Frontiers of Modern Education, vol. 6, no. 6, pp. 84-86, 2025.
Z. Li, N. Nie, S. Yu, et al., “A practical study on the intervention mechanism for psychological crisis among college students: a case study of Beijing Forestry University,” Chinese Forestry Education, vol. 43, no. 5, pp. 72-78, 2025.
G. Yu and X. Huang, “Comparative analysis of the detection rate of mental health problems among students: evidence from meta-analysis,” Educational Research, vol. 44, no. 6, pp. 105-121, 2023.
X. Wang, S. Pan, and D. Zhang, “The relationship between students’ psychological quality and depression: a three-level meta-analysis,” Journal of Southwest University (Social Sciences Edition), vol. 52, no. 1, pp. 261-273, 2026.
X. Jia and X. Wei, “Temporal modeling of student behavior and prediction method for mental health in students sensitive to abnormal behavior,” Journal of Beijing University of Technology, vol. 50, no. 8, pp. 939-947, 2024.