Sentiment Analysis Tweet KTT G-20 di Media Sosial Twitter Menggunakan Metode Naïve Bayes
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
The G-20 or The Group of Twenty is a group consisting of 19 countries with major economies plus 1 European Union. This group was formed in 1999 as a systematic forum with the aim of discussing important issues or problems related to the world economy. The countries included in the G-20 include Australia, Canada, Saudi Arabia, United States, India, Russia, South Africa, Turkey, Argentina, Brazil, Mexico, France, Germany, Italy, United Kingdom, China, India, Japan, and South Korea. From these data it can be concluded that the G-20 Summit is a forum capable of improving the standard of living of many people because of its ability to produce international policies, laws and regulations. Indonesia was once in the world's spotlight because in November 2022, Indonesia will host the G-20 Summit in Nusa Dua, Bali, to be precise. Ordinary people use Twitter to express emotions related to something, both negative and positive emotions. With the implementation of sentiment analysis data from tweets from 500 data tweets using the Naive Bayes algorithm, the result is an accuracy of 69%. The accuracy value with class precision for positive predictions is 78%, while the class precision accuracy value for negative predictions is 36%. The positive class recall accuracy value is 81%, while the negative class recall accuracy value is 32%.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it