Detecting Depression via Tweets on Twitter Utilizing Machine Learning and Neural Network Models
Depression is a widespread mental health condition that affects millions of people globally. However, early diagnosis remains challenging due to barriers such as limited access to health care. This study examines the identification and categorization of depression utilizing tweets from the Twitter p...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Bilijipub publisher
2025-06-01
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| Series: | Journal of Artificial Intelligence and System Modelling |
| Subjects: | |
| Online Access: | https://jaism.bilijipub.com/article_223863_4739e57222ce575210e8d1fc166a435f.pdf |
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| Summary: | Depression is a widespread mental health condition that affects millions of people globally. However, early diagnosis remains challenging due to barriers such as limited access to health care. This study examines the identification and categorization of depression utilizing tweets from the Twitter platform. Employing text analysis and classification methods, a binary classification model is developed to differentiate between depressed and non-depressed tweets. Eight diverse methods incorporating machine learning (ML) and deep learning (DL) are employed. Throughout the analysis process, the term frequency-inverse document frequency (TF-IDF), principal component analysis (PCA), and K-fold cross-validation methods are utilized. Subsequently, through a case study and various evaluation metrics, such as Accuracy, F1 Score, area under the receiver operating characteristic curve (AUC-ROC), Recall, and Precision, the accuracy of the models is assessed. The outputs reveal varying model performances across different metrics. For instance, the stochastic gradient descent (SGD) model exhibited the highest value of precision with a value of 1, while the neural network (NN) model outperformed others in terms of AUC-ROC and Recall with values of 0.9932 and 0.9718, respectively. Overall, the XGBoost (XGB) and NN models emerge as superior choices according to their performance across multiple evaluation criteria. This study highlights the potential of utilizing social media data for early depression detection, providing mental health professionals with valuable insights to identify at-risk individuals and implement timely interventions. |
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| ISSN: | 3041-850X |