Depression detection based on social networking sites using data mining

Social networking is becoming increasingly prevalent in today's globe. Young folks, senior citizens, and the general public use social media. However, over usage of social network communication harms human existence. In recent years, many mental diseases, such as reliance on cybernetic interactions, information overload, and network restriction, have been noticed in social networks. Currently, the indications of those mental diseases can be seen, posing several dangers. Human emotions, such as despair, are interior feelings that reveal an individual's actual behavior. Monitoring and detecting these thoughts from individual's choices in virtual social interactions could be very beneficial in realizing their behavior. This research develops a system that uses social media posts to detect individuals' depression. Data mining techniques have been employed to analyze social media data to identify individuals at risk of depression. It provides an overview of the major contributions made in the field of depression detection based on social networking sites using several machine learning (ML), deep learning, and data mining. We discuss the use of sentiment analysis (SA), natural language processing (NLP), ML, user profiling, and early warning systems. The paper highlights the potential benefits of using social media data for depression detection, including the ability to provide early intervention to individuals at risk of depression. However, there are also ethical considerations to be addressed, such as ensuring data privacy and avoiding stigmatization. The bag of words (BoW) approach is effective in capturing the sentiment and emotional tone of social media posts, which is crucial for detecting depression. It employs Naïve Bayes (NB) classifier which is is known for its accuracy in classifying text data for detecting the depression level. The implemented approach is compared with other SA methods concerning performance and results. In many evaluation parameters, the proposed method outperforms other SA systems. The approach provides runtime provision for adding new keywords and sentiments which adapts the approach to work fine for newest trends, wordings, and formats of posts. It suggests actions to be undertaken in case of detection of depressed users to control them drowning in further stress. Overall, this paper aims to provide a comprehensive understanding of the current state of research in depression detection based on social networking sites using data mining, and the potential implications for mental health care.