Mental Health Analysis Through Social Media Using Machine Learning and Deep Learning Approaches

Finally, the proposed system effectively shows how machine learning and deep learning can be used for recognizing mental health problems using social media data. The system uses different preprocessing methods to identify mental health cases, and then applies TF-IDF and classification algorithms like XGBoost and LSTM to achieve accurate predictions. The use of a user-friendly interface based on Django is another feature that adds value to the system. In addition to providing predictions, the system gives useful descriptions and solutions related to the problem. The visualization of results with charts is an important step in this process too. Thus, this system plays an important role in the early recognition of mental health problems and provides the basis for future research "Mental Health Analysis Through Social Media Using Machine Learning and Deep Learning Approaches" is a project that aims to analyze the mental condition of a person based on social media posts and data. It is essential to emphasize the importance of social media as a channel where people express their thoughts and feelings. Social media text data is analyzed through NLP operations, including preprocessing of posts and extracting features. Several Machine Learning algorithms, such as SVM, Random Forest, Naïve Bayes, and Logistic Regression, are compared with Deep Learning methods such as Long Short-Term Memory (LSTM). The experimental results indicate that automatic analysis helps to detect various symptoms of mental illness. It should be stated that the deep learning approach outperforms other machine learning algorithms since it allows for recognizing the context and sequence of words in text. Meanwhile, the latter option is much more productive in terms of time and performance. There are multiple benefits of utilizing automated analysis systems. These include fast identification of mental problems, the absence of human labor, scalability, and assistance for healthcare professionals. Challenges related to sarcasm, slang, and multilinguality may also occur.

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