TY - GEN
T1 - Covid-19 Tweets Analysis with Topic Modeling
AU - Jia, Shichao
AU - Chen, Qi
AU - Wang, Wei
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/11/27
Y1 - 2021/11/27
N2 - Social media has become an important data resource for knowledge discovery and data mining in multiple disciplines. With the exploding amount of social media data, how to efficiently and effectively exploit values and insights from such overwhelming amount of data has become an emerging area. Recently, various natural language processing techniques have been developed, e.g., word embedding, deep neural network and Latent Dirichlet Allocation (LDA), for studies such as sentiment analysis, traffic event detection, nature disaster assessment and COVID-19 tweet analysis. In this paper, topic modeling through LDA was used to conduct text mining on a large real-world COVID-19 tweet dataset, which contains more than 524 million multilingual tweets and covers 218 countries over a period of 3 months. We conducted extensive experiments and visualise insights discovered through this unsupervised process.
AB - Social media has become an important data resource for knowledge discovery and data mining in multiple disciplines. With the exploding amount of social media data, how to efficiently and effectively exploit values and insights from such overwhelming amount of data has become an emerging area. Recently, various natural language processing techniques have been developed, e.g., word embedding, deep neural network and Latent Dirichlet Allocation (LDA), for studies such as sentiment analysis, traffic event detection, nature disaster assessment and COVID-19 tweet analysis. In this paper, topic modeling through LDA was used to conduct text mining on a large real-world COVID-19 tweet dataset, which contains more than 524 million multilingual tweets and covers 218 countries over a period of 3 months. We conducted extensive experiments and visualise insights discovered through this unsupervised process.
KW - Covid-19
KW - LDA
KW - Social media analysis
UR - http://www.scopus.com/inward/record.url?scp=85127283504&partnerID=8YFLogxK
U2 - 10.1145/3507524.3507536
DO - 10.1145/3507524.3507536
M3 - Conference Proceeding
AN - SCOPUS:85127283504
T3 - ACM International Conference Proceeding Series
SP - 68
EP - 74
BT - ICCBD 2021 - 2021 4th International Conference on Computing and Big Data
PB - Association for Computing Machinery
T2 - 4th International Conference on Computing and Big Data, ICCBD 2021
Y2 - 27 November 2021 through 29 November 2021
ER -