Final Update and Summary for Detecting Depression in Social Media

This will be the final update and summary of my summer research project.

My project was detecting depression in social media using machine learning/ai techniques. The project consisted of collecting 50,000 comments collected from Reddit. Reddit is a social media site consisting of subreddits or subforums consisting of a theme. The subreddits I collected the comments from were /r/depression /r/depressed /r/suicidewatch /r/lonely for my experimental group and /r/frugal, /r/casualconversation, /r/relationships, /r/legaladvice, /r/AcomplishedToday, /r/wholesomememes. The data collection was done by a Python API Library called Praw and took about 2 hours to fully scrape.

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Update 2: Building the model

As of this update, I have finished most of the model building and metric analysis. Using the initial 2000 comments(1000 un-depressed, 1000 depressed), I cleaned out all unnecessary words(stop-words), punctuation, and removed all suffixes. Then I built a frequency table showing the most common two-word n-grams within the depressed class. N-grams are a combination of words next to each other. For example, “I like bees” the two-word ngrams, or bigrams, would be “I like”, “like bees”.

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Update 2: Model building

As of this update, I have finished model building and result analysis. Using the intial 2000 comments (1000 depressed 1000 undepressed) collected in the last update, I preprocessed the text by cleaning out all unnecessary words, characters (#), and punctuations. Then I built a frequency table that showed the most common two-word combinations in the text.

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Update 1: Learning machine learning

As originally stated in my abstract proposal https://freshmanmonroe.blogs.wm.edu/2018/04/10/assessing-antibiotic-sentiments-online-social-media/, I will be using artificial intelligence/machine learning to help detect depression in social media. I’m very excited to work on this project as it is able to combine my two passions., helping people and computer science, into one.

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