Analysis of Public Sentiment on Twitter Social Media the Design of the Latest Jersey of the Indonesian Football Team using the Support Vector Machine (SVM) Method
Bibliographic record
Abstract
Twitter has become a major platform for real-time public expression, including reactions to the Indonesian national football team’s new jersey released by Erspo on January 23, 2025. The previous edition had received strong criticism, creating the need to examine how the public responded to the new design. This study aims to analyze the distribution of sentiments on Twitter and evaluate the performance of the chosen classification method. The research employs Support Vector Machine (SVM) with a linear kernel to classify Indonesian-language tweets into positive and negative categories. Data were collected through crawling and processed using text preprocessing techniques such as case folding, tokenizing, filtering, and stemming, with features extracted using Term Frequency–Inverse Document Frequency (TF-IDF). The model’s performance was assessed based on accuracy, precision, and recall. Results show that public sentiment comprised 308 positive and 437 negative tweets. The SVM model achieved an accuracy of 82.35%, with 76% precision for positive and 86% precision for negative classifications. These results indicate that public responses tended to be negative, though positive appreciation was still evident. Overall, SVM proved effective for sentiment analysis and can provide valuable insights for decision-makers and jersey developers.
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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 | 0.002 | 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".