Detection of depressing tweets
Baba Bulls Eye·0 Submissions
Mental illness has been prevalent in the world, depression is one of the most common psychological problem i know and i would like to help as much as i can. Being a fan of Anthony Bourdain and Robin Williams, It has propel me to explore in this study. With the use of the Large amount of data tweets and Facebook post online i can use machine learning to data mine it and be able to produce a meaningful and useful outcome.
Social media generates countless data every day because of millions of active users share and communicate in entire community, it changes human interaction. For this project, I will be using Python and various modules and libraries.The aim of the project is to predict early signs of depression through Social Media text mining. Below are the steps to run the python codes using the data sets uploaded in the repositories or you can download your own.
Evaluation
What the result could mean? Postive, This mean that person is unlikely to have depression or anxiety. Neutral, This is the middle level wherein the user may or may not have depression but may also be more prone to being depress. At that stage the user may display some depression like symptoms. lasty, Negative is the lowest level where depression and anxiety symptoms are being detected through the users tweets. The more negative words the user uses mean the more negative emotion the tweet has.This study is not yet perfect and im still aiming to improve it.
- Use Contextual Semantic segmentation
- Use Stopwords to increase accuracy of model
- Eliminating features with extremely low frequency
- Use Complex Features: n-grams and part of speech tags