Mining Twitter Data on COVID-19 for Sentiment analysis and frequent patterns Discovery
Drias, H. H.; Drias, Y.
Show abstract
A study with a societal objective was carried out on people exchanging on social networks and more particularly on Twitter to observe their feelings on the COVID-19. A dataset of more than 600,000 tweets with hashtags like #COVID and #coronavirus posted between February 27, 2020 and March 25, 2020 was built. An exploratory treatment of the number of tweets posted by country, by language and other parameters revealed an overview of the apprehension of the pandemic around the world. A sentiment analysis was elaborated on the basis of the tweets posted in English because these constitute the great majority (USA, GB, India...). On the other hand, the FP-Growth algorithm was adapted to the tweets in order to discover the most frequent patterns and its derived association rules, in order to highlight the tweeters insights relatively to COVID-19.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Understanding mental health trends during COVID-19 pandemic in the United States using network analysis 93%
- Prediction of confirmed and death cases of Covid-19 in Chile through time series techniques: A comparative study 93%
- SARS-CoV-2 protein structure and sequence mutations: evolutionary analysis and effects on virus variants SARS-CoV-2 protein structure and sequence mutations: 92%
Similar papers in this journal
- Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features 94%
- A new, simple method of describing COVID-19 trajectory and dynamics in any country based on Johnson Cumulative Distribution Function fitting 93%
- Yet another lockdown? A large-scale study on people’s unwillingness to be confined during the first 5 months of the COVID-19 pandemic in Spain 91%
Similar papers in this journal
- Users’ Reactions on Announced Vaccines against COVID-19 Before Marketing in France: Analysis of Twitter posts 95%
- Fear of Infection and Sufficient Vaccine Reservation Information Might Drive Rapid Coronavirus Disease 2019 Vaccination in Japan: Evidence from Twitter Analysis 93%
- Abusers indoors and coronavirus outside: an examination of public discourse about COVID-19 and family violence on Twitter using machine learning 92%
Similar papers in this journal
- Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence 93%
- EnGRNT: Inference of gene regulatory networks using ensemble methods and topological feature extraction 91%
- Viral miRNAs Confer Survival in Host Cells by Targeting Apoptosis Related Host Genes 89%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.