Sentiment Analysis — Confusion Matrix & Metrics Calculator
Compute accuracy, precision, recall, F1, specificity, MCC and more for sentiment analysis from TP/FP/FN/TN counts.
Sentiment classifiers are usually evaluated with macro-F1 because positive and negative classes both matter and are often imbalanced in real review data. This calculator gives per-class and balanced views so you can see whether your model is quietly failing the minority sentiment.
Formula
Disclaimer: This tool is for general informational and estimation purposes only and is not professional financial, tax, accounting or legal advice. All figures are estimates — verify with a qualified professional before making decisions. Read the full disclaimer.
About Sentiment Analysis — Confusion Matrix & Metrics Calculator
Sentiment classifiers are usually evaluated with macro-F1 because positive and negative classes both matter and are often imbalanced in real review data. This calculator gives per-class and balanced views so you can see whether your model is quietly failing the minority sentiment. Enter the four confusion-matrix counts and this calculator returns every standard metric — accuracy, precision, recall (sensitivity), F1, specificity and the Matthews correlation coefficient — recomputed live. MCC is highlighted because it is the most honest single number for imbalanced problems: it only scores high when the model does well across all four quadrants, unlike accuracy or F1 which can be gamed.
How to use Sentiment Analysis — Confusion Matrix & Metrics Calculator
- 1Enter your values into Sentiment Analysis — Confusion Matrix & Metrics Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Sentiment Analysis — Confusion Matrix & Metrics Calculator?
- ✓Computes Sentiment Analysis instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: precision = TP/(TP+FP).
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Why use macro-F1 for sentiment?+
Macro-F1 averages each class's F1 equally, so a model that nails the majority sentiment but botches the minority can't hide behind overall accuracy. Since negative reviews are often rarer but more actionable, equal-weighted evaluation keeps the model honest on both.
How should neutral sentiment be handled?+
Either as a third class (then use multi-class macro-F1) or by thresholding model confidence into a neutral band. Forcing binary positive/negative on genuinely neutral text inflates both error types — this binary calculator assumes you've already separated neutral cases.
Why is MCC considered the most reliable single metric?+
MCC uses all four confusion-matrix cells and behaves like a correlation coefficient (−1 to +1): it is high only when predictions track reality across both classes. On imbalanced data where accuracy and even F1 can mislead, MCC stays informative — which is why it's increasingly the recommended summary statistic.
What's the difference between recall and specificity?+
Recall (sensitivity) is the fraction of actual positive sentiment cases the model catches — TP/(TP+FN). Specificity is the fraction of actual negative sentiment cases it correctly clears — TN/(TN+FP). A model can have high recall and low specificity (flags everything) or vice versa; you need both to judge it.
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