Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak
Bibliographic record
Abstract
Abstract The chronic nature of coronavirus disease (COVID-19) outbreak and lack of success in treatment and cure is creating an environment that is crucial for mental wellbeing. Presently, we extracted and classified sentiments and emotions from 141,208 headlines of global English news sources regarding the coronavirus disease (COVID-19). The headlines considered were those carrying keyword coronavirus between the time frame 15 Janaury, 2020 to 3 June, 2020 from top rated 25 English news sources. The headlines were classified into positive, negative and neutral sentiments after the calculation of text unbounded polarity at the sentence level score and incorporating the valence shifters. In addition, the National Research Council Canada (NRC) Word-Emotion Lexicon was used to calculate the presence of eight emotions at their emotional weight. The results reveal that the news headlines had high emotional scores with a negative polarity. More precisely, around 52% of the news headlines evoked negative sentiments and only 30% evoked positive sentiments while 18% were neutral. Fear, trust, anticipation, sadness, and anger were the main emotions evoked by the news headlines. Overall, the findings of this study can be weaved together into important implications for emotional wellbeing and economic perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".