Mood of India During Covid-19 -- An Interactive Web Portal Based on
Emotion Analysis of Twitter Data
- URL: http://arxiv.org/abs/2005.02955v1
- Date: Wed, 6 May 2020 17:04:43 GMT
- Title: Mood of India During Covid-19 -- An Interactive Web Portal Based on
Emotion Analysis of Twitter Data
- Authors: Akhila Sri Manasa Venigalla, Dheeraj Vagavolu and Sridhar Chimalakonda
- Abstract summary: This web portal aims to display mood of India during Covid-19, based on real time twitter data.
As of May 6 2020, the web portal has about 194370 tweets, and each of these tweets are classified into seven categories.
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.
- Score: 7.820667552233989
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
Related papers
- Covid-19 Public Sentiment Analysis for Indian Tweets Classification [0.0]
We show how Twitter data has been extracted and then run sentimental analysis queries on it.
This is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and are either positive or negative or neutral in some cases.
arXiv Detail & Related papers (2023-08-01T09:29:55Z) - Religion and Spirituality on Social Media in the Aftermath of the Global
Pandemic [59.930429668324294]
We analyse the sudden change in religious activities twofold: we create and deliver a questionnaire, as well as analyse Twitter data.
Importantly, we also analyse the temporal variations in this process by analysing a period of 3 months: July-September 2020.
arXiv Detail & Related papers (2022-12-11T18:41:02Z) - Why Do You Feel This Way? Summarizing Triggers of Emotions in Social
Media Posts [61.723046082145416]
We introduce CovidET (Emotions and their Triggers during Covid-19), a dataset of 1,900 English Reddit posts related to COVID-19.
We develop strong baselines to jointly detect emotions and summarize emotion triggers.
Our analyses show that CovidET presents new challenges in emotion-specific summarization, as well as multi-emotion detection in long social media posts.
arXiv Detail & Related papers (2022-10-22T19:10:26Z) - Extracting Feelings of People Regarding COVID-19 by Social Network
Mining [0.0]
dataset of COVID-related tweets in English language is collected.
More than two million tweets from March 23 to June 23 of 2020 are analyzed.
arXiv Detail & Related papers (2021-10-12T16:45:33Z) - American Twitter Users Revealed Social Determinants-related Oral Health
Disparities amid the COVID-19 Pandemic [72.44305630014534]
We collected oral health-related tweets during the COVID-19 pandemic from 9,104 Twitter users across 26 states.
Women and younger adults (19-29) are more likely to talk about oral health problems.
People from counties at a higher risk of COVID-19 talk more about tooth decay/gum bleeding and chipped tooth/tooth break.
arXiv Detail & Related papers (2021-09-16T01:10:06Z) - CML-COVID: A Large-Scale COVID-19 Twitter Dataset with Latent Topics,
Sentiment and Location Information [0.0]
CML-COVID is a COVID-19 Twitter data set of 19,298,967 million tweets from 5,977,653 unique individuals.
These tweets were collected between March 2020 and July 2020 using the query terms coronavirus, covid and mask related to COVID-19.
arXiv Detail & Related papers (2021-01-28T18:59:10Z) - Country Image in COVID-19 Pandemic: A Case Study of China [79.17323278601869]
Country image has a profound influence on international relations and economic development.
In the worldwide outbreak of COVID-19, countries and their people display different reactions.
In this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset.
arXiv Detail & Related papers (2020-09-12T15:54:51Z) - Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis
and Deep Learning -- a Case Study on COVID-19 [2.983310828879753]
Social media was bombarded with posts containing both positive and negative sentiments on the COVID-19, pandemic, lockdown, hashtags past couple of months.
This study tends to detect and analyze sentiment polarity and emotions demonstrated during the initial phase of the pandemic and the lockdown period.
arXiv Detail & Related papers (2020-08-23T12:43:26Z) - Analyzing COVID-19 on Online Social Media: Trends, Sentiments and
Emotions [44.92240076313168]
We analyze the affective trajectories of the American people and the Chinese people based on Twitter and Weibo posts between January 20th, 2020 and May 11th 2020.
By contrasting two very different countries, China and the Unites States, we reveal sharp differences in people's views on COVID-19 in different cultures.
Our study provides a computational approach to unveiling public emotions and concerns on the pandemic in real-time, which would potentially help policy-makers better understand people's need and thus make optimal policy.
arXiv Detail & Related papers (2020-05-29T09:24:38Z) - The Ivory Tower Lost: How College Students Respond Differently than the
General Public to the COVID-19 Pandemic [66.80677233314002]
Pandemic of the novel Coronavirus Disease 2019 (COVID-19) has presented governments with ultimate challenges.
In the United States, the country with the highest confirmed COVID-19 infection cases, a nationwide social distancing protocol has been implemented by the President.
This paper aims to discover the social implications of this unprecedented disruption in our interactive society by mining people's opinions on social media.
arXiv Detail & Related papers (2020-04-21T13:02:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.