Notebook:tweepy.ipynb -Used social media data from the Twitter API .Using the Tweepy python library I was able to extract close to 2000 tweets using relevant keywords. Used Natural Language Toolkit to preprocess and clean the tweets.
Notebook:tweet_emotions.ipynb Attached labels to the tweets ranging from (1-5) where 1 signified least likely to be depressed while 5 signified most likely to be depressed. Used 3 lexicons namely Affin,Vader and NRC to generate the scores . Performed a weighted average of the scores to correctly classify the tweets. From the NRC emotion lexicon, emotions which are common in a depressed individual like sadness and fear were assigned greater weights while emotions like Joy and Trust were penalised and assigned smaller weights.
model.ipynb Used Word2Vec to prepare a word embedding which was then fed to a simple LSTM network.