Öz
This study aims to detect depression-related content in social media posts automatically. The Sentiment140 dataset was used as the data source, with the text data preprocessed using natural language processing techniques. The processed texts were then converted into numerical vector representations using the GloVe word embedding method and analyzed with various classification algorithms. Deep learning models (CNN, LSTM, BiLSTM) and traditional machine learning algorithms (Naive Bayes, Random Forest, Decision Tree, SVM, Logistic Regression) were evaluated and compared for performance. Evaluation metrics included accuracy, precision, recall, and the F1 score. The experimental results show that deep learning models, particularly the CNN architecture, outperformed traditional classification methods in detecting depression-prone content. Overall, the study presents a practical and applicable approach for conducting mental health analysis based on social media data.
Anahtar Kelimeler
Depression Detection Social Media Analysis GloVe Deep Learning Machine Learning Text Classification Sentiment140 Sentiment Analysis
Destekleyen Kurum
This study was supported by the Scientific Research Projects Coordination Unit of Malatya Turgut Özal University under project number 25Y05.
Proje Numarası
25Y05
Teşekkür
This study was supported by the Scientific Research Projects Coordination Unit of Malatya Turgut Özal University under project number 25Y05.
Kaynakça
- Tanzi, L., Vezzetti, E., Moreno, R., & Moos, S. (2020). X-ray bone fracture classification using deep learning: a baseline for designing a reliable approach. Applied Sciences, 10(4), 1507
- Rajpurkar, P., Irvin, J., Bagul, A., Ding, D., Duan, T., Mehta, H., ... & Ng, A. Y. (2017). Mura: Large dataset for abnormality detection in musculoskeletal radiographs. arXiv preprint arXiv:1712.06957
- Sahin, M. E. (2023). Image processing and machine learning‐based bone fracture detection and classification using X‐ray images. International Journal of Imaging Systems and Technology, 33(3), 853-865.
- Agarwal, D., Singh, V., Singh, A. K., & Madan, P. (2025). Dwarf Updated Pelican Optimization Algorithm for Depression and Suicide Detection from Social Media. Psychiatric Quarterly, 1-34.
- Kumar, M., Dredze, M., Coppersmith, G., & De Choudhury, M. (2015, August). Detecting changes in suicide content manifested in social media following celebrity suicides. In Proceedings of the 26th ACM conference on Hypertext & Social Media (pp. 85-94).
- Yan, Z., Peng, F., & Zhang, D. (2025). DECEN: A deep learning model enhanced by depressive emotions for depression detection from social media content. Decision Support Systems, 114421.
- Nguyen, S. D., Tran, T. S., Tran, V. P., Lee, H. J., Piran, M. J., & Le, V. P. (2023). Deep learning-based crack detection: A survey. International Journal of Pavement Research and Technology, 16(4), 943-967.
- Uysal, F., Hardalaç, F., Peker, O., Tolunay, T., & Tokgöz, N. (2021). Classification of shoulder x-ray images with deep learning ensemble models. Applied Sciences, 11(6), 2723.
- Tejaswini, V., Sathya Babu, K., & Sahoo, B. (2024). Depression detection from social media text analysis using natural language processing techniques and hybrid deep learning model. ACM Transactions on Asian and Low-Resource Language Information Processing, 23(1), 1-20.
- Amin, A. A., Iqbal, M. S., & Shahbaz, M. H. (2024). Development of intelligent fault-tolerant control systems with machine learning, deep learning, and transfer learning algorithms: a review. Expert Systems with Applications, 238, 121956.
- Reddy, K. B., Naidu, D. S. P., Deekshitha, N., & Srinivas, M. (2025, February). Exploring Hybrid Approaches for Sentiment Classification: A Comparative Study of LSTM, Naive Bayes, and Bayesian Network on IMDB Reviews. In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE) (pp. 221-227). IEEE.
- Elkahwagy, D. M. A. S., Kiriacos, C. J., & Mansour, M. (2024). Logistic regression and other statistical tools in diagnostic biomarker studies. Clinical and Translational Oncology, 26(9), 2172-2180.
- Sun, Z., Wang, G., Li, P., Wang, H., Zhang, M., & Liang, X. (2024). An improved random forest based on the classification accuracy and correlation measurement of decision trees. Expert Systems with Applications, 237, 121549.
- Wang, Z., & Gai, K. (2024). Decision tree-based federated learning: a survey. Blockchains, 2(1), 40-60.
- Deshpande, A., Dubey, A., Dhavale, A., Navatre, A., Gurav, U., & Chanchal, A. K. (2024, April). Implementation of an nlp-driven chatbot and ml algorithms for career counseling. In 2024 International Conference on Inventive Computation Technologies (ICICT) (pp. 853-859). IEEE.
- Yao, W., Bai, J., Liao, W., Chen, Y., Liu, M., & Xie, Y. (2024). From cnn to transformer: A review of medical image segmentation models. Journal of Imaging Informatics in Medicine, 37(4), 1529-1547.
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
- Zhang, S., Zheng, D., Hu, X., & Yang, M. (2015, October). Bidirectional long short-term memory networks for relation classification. In Proceedings of the 29th Pacific Asia conference on language, information and computation (pp. 73-78).
- Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
Öz
Proje Numarası
25Y05
Kaynakça
- Tanzi, L., Vezzetti, E., Moreno, R., & Moos, S. (2020). X-ray bone fracture classification using deep learning: a baseline for designing a reliable approach. Applied Sciences, 10(4), 1507
- Rajpurkar, P., Irvin, J., Bagul, A., Ding, D., Duan, T., Mehta, H., ... & Ng, A. Y. (2017). Mura: Large dataset for abnormality detection in musculoskeletal radiographs. arXiv preprint arXiv:1712.06957
- Sahin, M. E. (2023). Image processing and machine learning‐based bone fracture detection and classification using X‐ray images. International Journal of Imaging Systems and Technology, 33(3), 853-865.
- Agarwal, D., Singh, V., Singh, A. K., & Madan, P. (2025). Dwarf Updated Pelican Optimization Algorithm for Depression and Suicide Detection from Social Media. Psychiatric Quarterly, 1-34.
- Kumar, M., Dredze, M., Coppersmith, G., & De Choudhury, M. (2015, August). Detecting changes in suicide content manifested in social media following celebrity suicides. In Proceedings of the 26th ACM conference on Hypertext & Social Media (pp. 85-94).
- Yan, Z., Peng, F., & Zhang, D. (2025). DECEN: A deep learning model enhanced by depressive emotions for depression detection from social media content. Decision Support Systems, 114421.
- Nguyen, S. D., Tran, T. S., Tran, V. P., Lee, H. J., Piran, M. J., & Le, V. P. (2023). Deep learning-based crack detection: A survey. International Journal of Pavement Research and Technology, 16(4), 943-967.
- Uysal, F., Hardalaç, F., Peker, O., Tolunay, T., & Tokgöz, N. (2021). Classification of shoulder x-ray images with deep learning ensemble models. Applied Sciences, 11(6), 2723.
- Tejaswini, V., Sathya Babu, K., & Sahoo, B. (2024). Depression detection from social media text analysis using natural language processing techniques and hybrid deep learning model. ACM Transactions on Asian and Low-Resource Language Information Processing, 23(1), 1-20.
- Amin, A. A., Iqbal, M. S., & Shahbaz, M. H. (2024). Development of intelligent fault-tolerant control systems with machine learning, deep learning, and transfer learning algorithms: a review. Expert Systems with Applications, 238, 121956.
- Reddy, K. B., Naidu, D. S. P., Deekshitha, N., & Srinivas, M. (2025, February). Exploring Hybrid Approaches for Sentiment Classification: A Comparative Study of LSTM, Naive Bayes, and Bayesian Network on IMDB Reviews. In 2025 International Conference on Artificial Intelligence and Data Engineering (AIDE) (pp. 221-227). IEEE.
- Elkahwagy, D. M. A. S., Kiriacos, C. J., & Mansour, M. (2024). Logistic regression and other statistical tools in diagnostic biomarker studies. Clinical and Translational Oncology, 26(9), 2172-2180.
- Sun, Z., Wang, G., Li, P., Wang, H., Zhang, M., & Liang, X. (2024). An improved random forest based on the classification accuracy and correlation measurement of decision trees. Expert Systems with Applications, 237, 121549.
- Wang, Z., & Gai, K. (2024). Decision tree-based federated learning: a survey. Blockchains, 2(1), 40-60.
- Deshpande, A., Dubey, A., Dhavale, A., Navatre, A., Gurav, U., & Chanchal, A. K. (2024, April). Implementation of an nlp-driven chatbot and ml algorithms for career counseling. In 2024 International Conference on Inventive Computation Technologies (ICICT) (pp. 853-859). IEEE.
- Yao, W., Bai, J., Liao, W., Chen, Y., Liu, M., & Xie, Y. (2024). From cnn to transformer: A review of medical image segmentation models. Journal of Imaging Informatics in Medicine, 37(4), 1529-1547.
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
- Zhang, S., Zheng, D., Hu, X., & Yang, M. (2015, October). Bidirectional long short-term memory networks for relation classification. In Proceedings of the 29th Pacific Asia conference on language, information and computation (pp. 73-78).
- Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Bilgisayar Yazılımı |
| Bölüm | Research Articles |
| Yazarlar | |
| Proje Numarası | 25Y05 |
| Erken Görünüm Tarihi | 29 Haziran 2025 |
| Yayımlanma Tarihi | 29 Haziran 2025 |
| Gönderilme Tarihi | 16 Mayıs 2025 |
| Kabul Tarihi | 23 Haziran 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 6 Sayı: 1 |
