Prediction of The Level of Public Trust in Government Policies in the 1<sup>st</sup>Quarter of The Covid 19 Pandemic using Sentiment Analysis
Bibliographic record
Abstract
The covid-19 pandemic has made changes in society, including Government policy. The policy changes led to mixing responses from the public, namely netizens. Netizen shares their opinion in social media, including Twitter. Their opinion can represent the public’s trust in the Government. Sentiment analysis analyses others’ opinions and categorises them into positive opinions, negative opinions, or neutral opinions. Sentiment analysis can analyze large numbers of opinions so that public opinion can be analyzed quickly. This paper explains how to analyze public trust using sentiment analysis and to use Naïve Bayes classification method to analyze sentiment. The data research was taken from Twitter in the first quarter of the Covid-19 pandemic, with around 3000 tweets. The tweets were related to Covid-19 and the Government from several countries such as the United States, Australia, Ireland, Switzerland, Italy, Philippines, Sri Lanka, Canada, Netherlands, United Kingdom, Germany, and Lebanon. This study aims to determine the level of public trust in the Government in the first quarter of the Covid-19 pandemic. The research result is expected to be used as a reference for the public policy stakeholders to determine future policies.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".