Prediction of Depression Using Machine Learning and NLP Approach

Today, for Internet users, micro-blogging has become a popular networking forum. Millions of people exchange views on different aspects of their lives. Thus, micro blogging websites are a rich source of opinion mining data or Sentiment Analysis (SA) information. Because of the recent advent of micro blogging, there are a few research papers dedicated to this subject. In our paper, we concentrate on Twitter, one of the leading micro blogging sites, to explore the opinion of the public. We will demonstrate how to collect real-time Twitter data and use algorithms such as Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words (BOW) and Multinomial Naive Bayes (MNB) for sentiment analysis or opinion mining purposes. We are able to assess positive and negative feelings using the algorithms selected above for the real-time twitter info. The following experimental evaluations show that the algorithms used are accurate and can be used as an application for diagnosing the depression of individuals. We worked with English in this post, but it can be used with any other language. English in this document, but it can be used for any other language.