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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Detecting Depression in Social Media using Machine Learning

Depression is one of the most afflicting mental health illnesses in America; however, with the sheer amount of data available in social media, machine learning, a field of artificial intelligence, can be used to detect early signs of depression. Machine learning is used in many industries today, most notably voice-to-text applications like Siri or Spotify song recommendations, however, its’ use in public health is just beginning to emerge.

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