Mental Health Of INDIA
TEAM : THE ELITE
Team : The Elite
PGP in Data Science @
Business Analytics @ IIM
Analytics @ IIM Indore
Student @ Vardhaman
- Can we analyze the mental health of a person based on his twitter
- If yes, what are the factors that determine this?
- To what extent COVID-19 affected people`s mental
- To come up with an effective methodology to analyze mental health
- To understand what determines the emotion conveyed in a tweet.
- To gather insights on how
COVID-19 affected mental health based on tweets.
Observations : Labelling tweets
- % of tweets with emojis and hashtags which correspond to an emotion are less.
- Less correlation between emotions extracted from emojis,hashtags and polarity of
- For every tweet we could extract whether a particular emotion is present in the
tweet or not.
- The unsupervised approach using emotion
lexicons is relatively faster.
- We just need a single scan of the dataset to
label each tweet with a particular emotion.
- Thanks to the emotion lexicons, we could label each tweet with multiple emotions.
- We have built 6 binary classification models, where each model
a particular emotion.
- To build these models, Logistic regression was used over the vector
embeddings extracted using the lexicon database.
- Yes, we can determine the mental health of a person
using twitter usage.
- The overall emotions in a tweet are decided by the emotions of
individual words and not
hashtags or emojis.
- COVID-19 has definitely taken a toll on people`s mental health as fear and sadness seem to be
dominating their emotional state.
- The predictions of our model are accurate only for tweets that use vocabulary similar to
that of our training set.
- If none of the words in a tweet are part of our training set vocabulary, then it is
implicitly labelled as neutral.
- To overcome the above limitations, we can train on a larger dataset.
Time of the day the tweets were posted
Most used Hashtags
Number of tweets belonging to each emotion
Correlation between emotions extracted from emojis v/s Polarity of the tweet