Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic With Deep Learning
Bibliographic record
Abstract
Due to the COVID-19 pandemic, vaccine development and community vaccination studies are carried out all over the world. At this stage, the opposition to the vaccine seen in the society or the lack of trust in the developed vaccine is an important factor hampering vaccination activities. In this study, aspect-base sentiment analysis was conducted for USA, U.K., Canada, Turkey, France, Germany, Spain and Italy showing the approach of twitter users to vaccination and vaccine types during the COVID-19 period. Within the scope of this study, two datasets in English and Turkish were prepared with 928,402 different vaccine-focused tweets collected by country. In the classification of tweets, 4 different aspects (policy, health, media and other) and 4 different BERT models (mBERT-base, BioBERT, ClinicalBERT and BERTurk) were used. 6 different COVID-19 vaccines with the highest frequency among the datasets were selected and sentiment analysis was made by using Twitter posts regarding these vaccines. To the best of our knowledge, this paper is the first attempt to understand people's views about vaccination and types of vaccines. With the experiments conducted, the results of the views of the people on vaccination and vaccine types were presented according to the countries. The success of the method proposed in this study in the F1 Score was between 84% and 88% in datasets divided by country, while the total accuracy value was 87%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".