Managing demand volatility during unplanned events with sentiment analysis: a case study of the COVID-19 pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unplanned events such as natural disasters or epidemic outbreaks are usually accompanied by supply chain disruption and highly volatile markets. Besides, the recent COVID-19 crisis has shown that existing artificial intelligence systems and data analytics models, which normally provide valuable support in demand forecasting, have not been able to manage demand volatility. This study contributes addressing this issue and aims to determine whether sentiments conveyed by news media influence consumer behavior. It provides a case study conducted in three steps: (1) data were collected and prepared; (2) a sentiment analysis model was developed; and (3) a statistical analysis was performed to analyze the correlation between sentiments in news and drug consumption during the COVID-19 crisis. Findings highlighted a strong positive correlation between sentiments in news and consumption variability. They therefore suggest that sentiments in news have strong predictive power for demand forecasting in unplanned situations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it