Topic extraction and sentiment analysis of subreddit (r/Coronavirus)
As the COVID-19 pandemic hits the one-year mark, this study takes a look at the content on Reddit’s COVID-19 community, r/Coronavirus. The aim of this study was to gain insight on the public’s sentiments towards COVID-19, the topics that emerged, as well as how they have changed during the course of...
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2021
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sg-ntu-dr.10356-1481532021-04-24T06:16:31Z Topic extraction and sentiment analysis of subreddit (r/Coronavirus) Tan, Zachary Meng Jie Anwitaman Datta School of Computer Science and Engineering Anwitaman@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence As the COVID-19 pandemic hits the one-year mark, this study takes a look at the content on Reddit’s COVID-19 community, r/Coronavirus. The aim of this study was to gain insight on the public’s sentiments towards COVID-19, the topics that emerged, as well as how they have changed during the course of the pandemic. Based on 356,690 submissions and 9,413,331 comments collected between 20th January 2020 and 31st January 202, analysis was subsequently conducted on each dataset based on lexical sentiment and topics generated from unsupervised topic modelling. The study found that negative sentiments show higher ratio in submissions while negative sentiments were of the same ratio as positive ones in the comments. Terms associated more positively or negatively were identified. Upon assessment of the upvotes and downvotes, this study also uncovered contentious topics such as “fake news”. Through topic modelling, 9 distinct topics were identified from submissions while 20 were identified from comments. Overall, this study provided a clear overview on the dominating topics and popular sentiments that would provide governments and health authorities a deeper understanding of the public’s concerns throughout the year. Bachelor of Engineering (Computer Science) 2021-04-24T06:16:30Z 2021-04-24T06:16:30Z 2021 Final Year Project (FYP) Tan, Z. M. J. (2021). Topic extraction and sentiment analysis of subreddit (r/Coronavirus). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148153 https://hdl.handle.net/10356/148153 en SCSE20-0564 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Tan, Zachary Meng Jie Topic extraction and sentiment analysis of subreddit (r/Coronavirus) |
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As the COVID-19 pandemic hits the one-year mark, this study takes a look at the content on Reddit’s COVID-19 community, r/Coronavirus. The aim of this study was to gain insight on the public’s sentiments towards COVID-19, the topics that emerged, as well as how they have changed during the course of the pandemic. Based on 356,690 submissions and 9,413,331 comments collected between 20th January 2020 and 31st January 202, analysis was subsequently conducted on each dataset based on lexical sentiment and topics generated from unsupervised topic modelling. The study found that negative sentiments show higher ratio in submissions while negative sentiments were of the same ratio as positive ones in the comments. Terms associated more positively or negatively were identified. Upon assessment of the upvotes and downvotes, this study also uncovered contentious topics such as “fake news”. Through topic modelling, 9 distinct topics were identified from submissions while 20 were identified from comments. Overall, this study provided a clear overview on the dominating topics and popular sentiments that would provide governments and health authorities a deeper understanding of the public’s concerns throughout the year. |
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Anwitaman Datta |
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Anwitaman Datta Tan, Zachary Meng Jie |
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Final Year Project |
author |
Tan, Zachary Meng Jie |
author_sort |
Tan, Zachary Meng Jie |
title |
Topic extraction and sentiment analysis of subreddit (r/Coronavirus) |
title_short |
Topic extraction and sentiment analysis of subreddit (r/Coronavirus) |
title_full |
Topic extraction and sentiment analysis of subreddit (r/Coronavirus) |
title_fullStr |
Topic extraction and sentiment analysis of subreddit (r/Coronavirus) |
title_full_unstemmed |
Topic extraction and sentiment analysis of subreddit (r/Coronavirus) |
title_sort |
topic extraction and sentiment analysis of subreddit (r/coronavirus) |
publisher |
Nanyang Technological University |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/148153 |
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1698713709910687744 |