Textual Analysis for Public Sentiment Toward National Police Using CRISP-DM Framework
Bibliographic record
Abstract
Nowadays, public opinion toward the National Police's (POLRI) image is deteriorating.With the explosive growth of social media in Indonesia, opinions on POLRI-related present-day issues on Twitter easily go viral, influencing sentiments among individuals regarding Indonesian law enforcement.Negative sentiments, at some point, may lead to the undervaluation of law enforcement and the failure of the legal system.Therefore, sentiment analysis on Twitter is essential for gaining considerable insights into public views and attitudes on POLRI-related topics.This research is to determine the most effective approaches between Lexicon, a natural language processing method that relies on a corpus, and machine learning, which contains Naive-Bayes, Support Vector Machine (SVM), Random Forest, and Logistic Regression (LR).These approaches have differences in classification types: probability and linearity.To organize the research process, the Cross-Industry Standard Process for Data Mining (CRISP-DM) Framework, which comprises five data mining activities, was employed.The confusion matrix was used as the model performance measurement, with Naive-Bayes emerging as the best among all the tested models.Additionally, the subjects related to POLRI were developed using topic modeling, generating three topics: street police or police station, police acknowledgment in neighborhood activities, and the activity of contacting the police.
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How this classification was reachedexpand
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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".