Sentiment Analysis: Classifying Public Comments on YouTube in Disaster Management Simulation in Indonesia Using Naïve Bayes and Support Vector Machine
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 objective of this research is to classify public comments on YouTube related to disaster preparedness simulations through sentiment analysis.The research process included data collection, labeling, pre-processing, and classification.Support Vector Machine (SVM) and Naï ve Bayes algorithms were used for classification.Following manual labeling of 204 datasets, the breakdown of sentiment was as follows: 112 positive, 43 negative, and 49 neutral.The evaluation involved two scenarios: performance testing and sensitivity testing.Performance testing, conducted on pre-processed datasets, revealed that Naï ve Bayes Classifier (NBC) achieved an accuracy rate of 80.4%, with the best execution time of 0.0097 seconds.In contrast, the Support Vector Machine (SVM) achieved the highest accuracy rate of 72.3%, albeit with a longer worst-case execution time of 193.48 seconds.Furthermore, in the results of sensitivity measurements using the dataset without going through the preprocessing stages, each method was able to show the best results with a value of 100%.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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