Large Dataset of Nigeria Covid-19 Tweets for Sentiment Analysis and Opinion Mining Tasks
Creators
- 1. Federal University of Agriculture, Abeokuta, Nigeria.
- 2. Department of Computer Science and Communication, Østfold University College, 1757 Halden, Norway
- 3. Department of Software Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
Contributors
Data collectors:
Project leader:
- 1. Federal University of Agriculture, Abeokuta, Nigeria.
Description
Background
Information is essential for growth; without it, little can be accomplished. Data gathering has seen significant changes throughout the previous few centuries because of certain transitory medium. The look and style of information transference are affected by the employment of new and emerging technologies, some of which are efficient, others are reliable, and many more are quick and effective, but a few were disappointing for various reasons.
Aims
This study aims at using TextBlob and VADER analyser with historical tweets, to analyse emotional responses to the corona virus pandemic (covid-19). It shows us how much of a sociological, environmental, and economic impact it has in Nigeria, among other things. This study would be a tremendous step forward for students, researchers, and scholars who want to advance in fields like data science, machine learning, and deep learning.
Methodology
The hashtag ‘covid-19' was used to collect 1,048,575 tweets from Twitter. The tweets were pre-processed with a twitter tokenizer, and Valence Aware Dictionary for Sentiment Reasoning (VADER) and TextBlob were used for sentiment and text mining, respectively. Topic modelling was done with Latent Dirichlet Allocation (LDA). The simulated subjects, on the other hand, were visualized using Multidimensional scaling (MDS).
Results
The result of the VADER sentiment returned 39.8%, 31.3% and 28.9%, positive, neutral, and negative sentiment respectively while the result of the TextBlob sentiment returned 46.0%, 36.7% and 17.3%, neutral, positive, and negative sentiment, respectively.
Conclusion
With all of this, information from social media may be used to help organizations, governments, and nations around the world make smart and effective decisions about how to restrict and limit the negative effects of covid-19. Also know the opinion and challenges of people, then deal with problem of misinformation.
It is concluded that with popular belief a significant number of the populace regards covid-19 as a virus that has come to stay, some believe it will eventually be conquered.
Files
Tweets Content Only.csv
Files
(93.9 MB)
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Additional details
Related works
- Cites
- Journal article: 10.3390/ info13030152 (DOI)
- Is supplement to
- Dataset: https://zenodo.org/record/4748717 (URL)