Spotle AI-thon - The AI Global Challenge 7000 participants had teams from best campuses in India, Singapore worked on addressing the mental health challenge with AI. Top 10 teams from IIT Roorkee, CMI, NIT, IIM Indore made it to the final round. This is a showcase Top 10 presentation from Team Jarvis (AVIRUP CHAKRABORTY Chennai Mathematical Institute ROHAN GUPTA Indian Institute of Technology Roorkee)

1. ANALYSIS OF
THE MENTAL HEALTH OF INDIA
DURING COVID-19
USING TWITTER DATA
Team : JARVIS
AVIRUP CHAKRABORTY
Chennai Mathematical Institute
ROHAN GUPTA
Indian Institute of Technology Roorkee
Spotle AI-Thon 2020

2. INTRODUCTION
❖Social Media has become a powerful platform for the public to voice their concerns
and beliefs. Among them one such platform is
Twitter has been a popular platform for microblogging in the past few years.
❖Due to the COVID-19 pandemic, there has been a wide change in sentiments of
various sectors of the Indian public towards the government policies/actions.
Studying the sentiment of the people on the epidemic and government decisions is
very important as it acts as a sanity check for the effectiveness of the adopted
government policies.
❖In this context, Natural Language Processing and Sentiment Analysis are extremely
useful in social media monitoring as they allow us to gain an overview of the wider
public opinion behind certain topics.

3. PROBLEM STATEMENT
❖Use Natural Language Processing and Sentiment Analysis to
classify COVID 19 tweets based on emotions.
❖Perform Exploratory Data Analysis and draw Conclusions to
analyze the Mental Health of India amidst Covid-19.

4. METHODOLOGY
❖ To build a dataset for training the initial model(s), we considered three different datasets (DailyDialog,
ISEAR, and Emotion-Stimulus) containing dialogues/situations from individuals and corresponding state
of emotions they depict. We chose five classes:
Anger, Fear, Joy, Sad, Neutral
❖ We followed mainly three approaches to train the data. The performances of these approaches were
compared using their corresponding F1-score which were as follows:
❖ Based on the F1 scores, we chose the CNN model for predictions.
Approach F1-score
SVM 0.727
LSTM 0.739
CNN+Embedding 0.832
BERT 0.801

5. EXPLORATORY DATA ANALYSIS
Word Cloud for top prevalent words in India Tweet Count Trend in India

7. RESULTS & FINDINGS
❖Model trained was used to make predictions on the given dataset. Some examples of the predictions on
the cleaned tweets are :

8. ❖The counts for each of the emotion class and their corresponding percentages
are tabulated below:
Emotion Count Percentage (%)
Fear 52383 55.84
Neutral 9687 15.01
Joy 8751 11.63
Anger 8111 9.91
Sadness 5314 7.61

9. CONCLUSION
❖Most tweets have been classified in ‘Fear’ class.
This can be attributed to the fear in the minds of people when they tweet
about the unprecedented rise in COVID- 19 cases. It is quite comprehensible
in this situation of pandemic that the dominant emotion is - fear.
❖Next majority tweets belong to ‘Neutral’ class.
These tweets serve as a medium of information transfer about recent statistics of deaths, active cases and
recoveries in the pandemic.
❖The other three classes, namely ‘Joy’, ‘Anger’ and ‘Sadness’, share almost equal proportions which can
respectively be attributed to:
➢increase in recoveries and corona vaccine
➢mismanagement of the government or the insensitive behaviour of citizens towards pandemic
➢some personal loss or deaths of people in general.

10. THANK YOU