Benchmark Surface
Model Evaluation
Compare supported sentiment models across available corpora, inspect confusion structure, and review class-level behavior before using them in the public dashboard.
Dataset
Corpus
Switch between evaluation corpora without leaving the page or changing chart semantics.
Selection
Model
Choose a single model to drive the confusion matrix and per-class visual diagnostics.
Snapshot
Summary
Key model quality metrics for the currently selected corpus and evaluation target.
Dataset: News Corpus
ModelNaive BayesSelected evaluation target
Accuracy46.7%Overall classification accuracy
Precision27.8%Macro average
Recall46.7%Macro average
F133.3%Macro average
Visuals
Evaluation Visuals
Read prediction concentration and per-class tradeoffs side-by-side for the active model.
Reference Table
All Model Details
A full comparison table for the loaded corpus, including confusion matrix shape.
| Model | Accuracy | Precision (macro) | Recall (macro) | F1 (macro) | Confusion Matrix Shape |
|---|---|---|---|---|---|
| Naive Bayes | 46.7% | 27.8% | 46.7% | 33.3% | 3 x 3 |
| SVM | 46.7% | 28.9% | 46.7% | 34.4% | 3 x 3 |
| VADER | 80.0% | 70.0% | 80.0% | 73.3% | 3 x 3 |
| OpenAI | 100.0% | 100.0% | 100.0% | 100.0% | 3 x 3 |