Mood of India During Covid-19 -- An Interactive Web Portal Based on Emotion Analysis of Twitter Data
Not on the map. Why?
Abstract (arXiv)
The severe outbreak of Covid-19 pandemic has affected many countries across the world, and disrupted the day to day activities of many people. During such outbreaks, understanding the emotional state of citizens of a country could be of interest to various organizations to carry out tasks and to take necessary measures. Several studies have been performed on data available on various social media platforms and websites to understand the emotions of people against many events, inclusive of Covid-19, across the world. Twitter and other social media platforms have been bridging the gap between the citizens and government in various countries and are of more prominence in India. Sentiment Analysis of posts on twitter is observed to accurately reveal the sentiments. Analysing real time posts on twitter in India during Covid-19, could help in identifying the mood of the nation. However, most of the existing studies related to Covid-19, on twitter and other social media platforms are performed on data posted during a specific interval. We are not aware of any research that identifies emotional state of India on a daily basis. Hence, we present a web portal that aims to display mood of India during Covid-19, based on real time twitter data. This portal also enables users to select date range, specific date and state in India to display mood of people belonging to the specified region, on the specified date or during the specified date range. Also, the number of Covid-19 cases and mood of people at specific cities and states on specific dates is visualized on the country map. As of May 6 2020, the web portal has about 194370 tweets, and each of these tweets are classified into seven categories that include six basic emotions and a neutral category. A list of Trigger Events are also specified, to allow users to view the mood of India on specific events happening in the country during Covid-19.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 May 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 2.
5 papers.
- 2023Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPoxcited by 8
- 2024Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccinationcited by 1
- 2025SenWave: A Fine-Grained Multi-Language Sentiment Analysis Dataset Sourced from COVID-19 Tweetscited by 1
- 2021Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolationcited by 0
- 2025Early Detection of Mental Health Issues Using Social Media Postscited by 0
- 2025SenWave: A Fine-Grained Multi-Language Sentiment Analysis Dataset Sourced from COVID-19 Tweets9 Oct 2025
- 2025Early Detection of Mental Health Issues Using Social Media Posts6 Mar 2025
- 2024Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination15 Jun 2024
- 2023Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox17 Dec 2023
- 2021Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation16 Jan 2021