A study on Covid-19 tweets
The spread of infectious diseases has caused economic and human losses throughout history. The outbreak of the COVID-19 virus changed the lives of people around the world. Social media, such as Twitter, have played a significant role in providing the general public with the latest information and...
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sg-ntu-dr.10356-1629112022-11-14T02:14:38Z A study on Covid-19 tweets Nur Lydia Afiqah Binte Rozali Sun Aixin School of Computer Science and Engineering AXSun@ntu.edu.sg Engineering::Computer science and engineering::Data The spread of infectious diseases has caused economic and human losses throughout history. The outbreak of the COVID-19 virus changed the lives of people around the world. Social media, such as Twitter, have played a significant role in providing the general public with the latest information and policy updates related to COVID-19 throughout the pandemic and government bodies [3]. Twitter is a platform for users to express their opinions and views. With the help of this microblogging platform, data can be extracted that can be used for sentiment and emotional analysis, a technique that is referred to as natural language processing (NLP). Over 14 months from 1 February 2020 to 31 March 2021, two billion multilingual tweets related to the COVID-19 pandemic were extracted and analyzed in a previous study [5]. This study will explore the data collection of tweets collected for COVID-19 from TBCOV [5]. The focus will be on the evolution of COVID-19 and understanding the trends of sentiments posted by Twitter users throughout South-East Asia, with a centering on Singapore, Thailand, Myanmar, and Vietnam during the COVID-19 pandemic. The results of the most indicative terms and named entities will be discussed using the Relative Entropy formula. Bachelor of Engineering (Computer Science) 2022-11-14T02:14:37Z 2022-11-14T02:14:37Z 2022 Final Year Project (FYP) Nur Lydia Afiqah Binte Rozali (2022). A study on Covid-19 tweets. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162911 https://hdl.handle.net/10356/162911 en SCSE21-0717 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering::Data Nur Lydia Afiqah Binte Rozali A study on Covid-19 tweets |
description |
The spread of infectious diseases has caused economic and human losses throughout history. The
outbreak of the COVID-19 virus changed the lives of people around the world. Social media,
such as Twitter, have played a significant role in providing the general public with the latest
information and policy updates related to COVID-19 throughout the pandemic and government
bodies [3].
Twitter is a platform for users to express their opinions and views. With the help of this
microblogging platform, data can be extracted that can be used for sentiment and emotional
analysis, a technique that is referred to as natural language processing (NLP). Over 14 months
from 1 February 2020 to 31 March 2021, two billion multilingual tweets related to the
COVID-19 pandemic were extracted and analyzed in a previous study [5].
This study will explore the data collection of tweets collected for COVID-19 from TBCOV [5].
The focus will be on the evolution of COVID-19 and understanding the trends of sentiments
posted by Twitter users throughout South-East Asia, with a centering on Singapore, Thailand,
Myanmar, and Vietnam during the COVID-19 pandemic. The results of the most indicative terms
and named entities will be discussed using the Relative Entropy formula. |
author2 |
Sun Aixin |
author_facet |
Sun Aixin Nur Lydia Afiqah Binte Rozali |
format |
Final Year Project |
author |
Nur Lydia Afiqah Binte Rozali |
author_sort |
Nur Lydia Afiqah Binte Rozali |
title |
A study on Covid-19 tweets |
title_short |
A study on Covid-19 tweets |
title_full |
A study on Covid-19 tweets |
title_fullStr |
A study on Covid-19 tweets |
title_full_unstemmed |
A study on Covid-19 tweets |
title_sort |
study on covid-19 tweets |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/162911 |
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1751548578627584000 |