Skip to main content

         This documentation site is for previous versions. Visit our new documentation site for current releases.      

Text analytics accuracy measures

Updated on May 17, 2024

Models predict an outcome, which might or might not match the actual outcome. The following measures are used to examine the performance of text analytics models. When you create a sentiment or classification model, you can analyze the results by using the performance measures that are described below.

True positives

The total number of outcomes that are predicted correctly, that is, the predicted outcome matches the actual outcome.

Actual count

The total number of times when a text is classified with this actual outcome, the expected outcome.

Predicted count

The total number of times when the model predicted a text to belong to this outcome.


The fraction of predicted instances that are correct. Precision measures the exactness of a classifier. A higher precision means less false positives, while a lower precision means more false positives. The most effective way to improve precision is to decrease recall.

The following formula is used to determine the precision of a classifier: precision = true positives / predicted count


The fraction of correctly predicted instances. Recall measures the completeness, or sensitivity, of a classifier. Higher recall means less false negatives, while lower recall means more false negatives. Improving recall can often decrease precision because it gets increasingly harder to be precise as the sample space increases.

The following formula is used to determine the recall of a classifier: recall = true positives / actual count


Precision and recall can be combined to produce a single metric known as F-measure, which is the weighted harmonic mean of precision and recall.

The following formula is used to determine the F-score of a classifier: F-score = 2 * precision * recall / (precision + recall)

Have a question? Get answers now.

Visit the Support Center to ask questions, engage in discussions, share ideas, and help others.

Did you find this content helpful?

Want to help us improve this content?

We'd prefer it if you saw us at our best. is not optimized for Internet Explorer. For the optimal experience, please use:

Close Deprecation Notice
Contact us