Covid-19 Tweets Analysis with Topic Modeling
4th International Conference on Computing and Big Data, ICCBD 2021
; : 68-74, 2021.
Article
in English
| Scopus | ID: covidwho-1784901
ABSTRACT
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. © 2021 ACM.
Covid-19; LDA; Social media analysis; Data mining; Deep neural networks; Large dataset; Sentiment analysis; Social networking (online); Data resources; Knowledge discovery and data minings; Language processing techniques; Latent Dirichlet allocation; Multiple disciplines; Social media; Social media datum; Topic Modeling; Statistics
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
4th International Conference on Computing and Big Data, ICCBD 2021
Year:
2021
Document Type:
Article
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