Sentiment Analysis: Classifying Public Comments on YouTube in Disaster Management Simulation in Indonesia Using Naïve Bayes and Support Vector Machine
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 Na 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 Na 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%.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".