Application of Sentiment Analysis to Crime News using Tf-Idf and K-Nearest Neighbor to Assess Public Perception
Bibliographic record
Abstract
The development of information technology and social media has changed the way people access news, including criminal news that is often in the public spotlight. Criminal news not only presents facts, but can also shape public opinion quickly and widely so that it has the potential to cause disinformation. For this reason, sentiment analysis is needed that is able to provide an objective picture of public perception of criminal news.This study uses a quantitative approach with stages: collection of crime news data and public comments from online media, text preprocessing (cleansing, case folding, tokenizing, stopword removal, normalization, and stemming), feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), sentiment classification with the K-Nearest Neighbor algorithm (K-NN), as well as model evaluation through accuracy, precision, recall, and F1-score metrics. The results showed that the combination of TF-IDF and K-NN was able to classify public comments on criminal news into three sentiment classes (positive, negative, neutral) with an accuracy rate of 82%. Further evaluation showed an average precision value of 0.86, a recall of 0.82, and an F1-score of 0.82. These findings prove that the TF-IDF and K-NN methods are effective in understanding public perception of online media-based crime reporting.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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 | 0.000 |
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 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".