PREDICTING MENTAL HEALTH CONDITIONS THROUGH SOCIAL MEDIA ACTIVITY: DATA MINING TECHNIQUES
Keywords:
Data Mining, Mental Health, social media, Twitter, Classification Models, Sentiment Analysis, Natural Language Processing, Decision Tree, Random Forest.Abstract
The use of social media platforms to gather real-time information has proven to be an effective tool for identifying various mental health problems early on. This study aimed to classify mental health conditions using data mining techniques by analyzing tweets related to mental health disorders retrieved with various keywords and pre-processed with sentiment analysis and natural language processing techniques. Using different data mining algorithms, including decision tree and random forest classifiers, the developed models accurately predicted mental health conditions such as depression, anxiety disorders, schizophrenia, drug abuse, and seasonal emotional disorders. By tracking social media activities in real-time, the developed models could help monitor mental health and recognize mental health problems early on. The potential benefits of data mining strategies in healthcare include personalized treatment options, evidence-based decision making, and new discoveries. Overall, the study highlights the potential of data mining techniques in identifying mental health conditions early and improving mental health care.
