Public Sentiment Analysis on Facebook Posts About Shin Tae-Yong's Dismissal Using the K-Nearest Neighbors Algorithm
Bibliographic record
Abstract
The sacking of Indonesian national team coach Shin Tae-yong by PSSI on January 6, 2025 sparked massive public attention on various social media platforms, one of which was Facebook. The large volume of unstructured opinions required analysis to accurately understand public perception. This study aims to classify public sentiment toward the news of Shin Tae-yong's dismissal using the K-Nearest Neighbors (K-NN) machine learning method. The data used consists of public comments from Facebook, processed through a series of text preprocessing steps and word weighting using TF-IDF. The K-NN model was tested with a value of k = 80. The results show that the classification model achieved an overall accuracy rate of 76%. While the model performed well for positive and negative sentiment classes, its performance was very weak in identifying neutral sentiment (recall 0.02). The sentiment distribution results indicate that public opinion is dominated by positive sentiment at 56.5%, followed by negative sentiment (29.3%), and neutral sentiment (14.2%). The main finding of this study, which contradicts common assumptions, is that the public response on Facebook to this policy is predominantly positive.
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 | 0.001 | 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) | 0.001 | 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".