Location-Based Sentiment Analysis using Bayesian Networks on COVID-19 Twitter Data
Bibliographic record
Abstract
The onset of the COVID-19 pandemic and the various variants within it have caused mass public opinion, and many turn to web services and social media platforms to easily and quickly voice these opinions.Of these services and platforms, the microblogging platform Twitter has been widely used by people around the world to voice their opinions on the COVID-19 pandemic.Twitter poses unique challenges which stem from the 280 character limit imposed on a given tweet.Further, advances in technology allow for a precise location to be determined upon the submission of a tweet.This thesis deploys the tree augmented naïve Bayes (TAN) classifier sentiment analysis technique to analyze the sentiment towards and between the B.1.1.529BA.1 Omicron and B.1.617.2Delta variants of the COVID-19 pandemic across provinces and territories within Canada.It was believed that the sentiment would vary across Canadian provinces/territories within a given variant due to provincial/territorial COVID-19 measures and restrictions, and would vary across variants due to the di erence in severity and transmissibility of each variant, however it was observed that sentiment varied across provinces/territories for only some variants and varied across variants for only some provinces/territories. I dedicate this thesis to my dogs, Maya and Pippa. Every day you bring me happiness, love, and comfort.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".