Tone Consistency Evaluation ✅

This example demonstrates how to use Kastrax’s Tone Consistency metric to evaluate emotional tone patterns and sentiment consistency in text.

Overview ✅

The example shows how to:

Configure the Tone Consistency metric
Compare sentiment between texts
Analyze tone stability within text
Handle different tone scenarios

Setup ✅

Dependencies

Import the necessary dependencies:

src/index.ts


import { ToneConsistencyMetric } from '@kastrax/evals/nlp';

Metric Configuration ✅

Set up the Tone Consistency metric:

src/index.ts


const metric = new ToneConsistencyMetric();

Example Usage ✅

Consistent Positive Tone Example

Evaluate texts with similar positive sentiment:

src/index.ts


const input1 = 'This product is fantastic and amazing!';
const output1 = 'The product is excellent and wonderful!';
 
console.log('Example 1 - Consistent Positive Tone:');
console.log('Input:', input1);
console.log('Output:', output1);
 
const result1 = await metric.measure(input1, output1);
console.log('Metric Result:', {
  score: result1.score,
  info: result1.info,
});
// Example Output:
// Metric Result: {
//   score: 0.8333333333333335,
//   info: {
//     responseSentiment: 1.3333333333333333,
//     referenceSentiment: 1.1666666666666667,
//     difference: 0.16666666666666652
//   }
// }

Tone Stability Example

Evaluate sentiment consistency within a single text:

src/index.ts


const input2 = 'Great service! Friendly staff. Perfect atmosphere.';
const output2 = ''; // Empty string for stability analysis
 
console.log('Example 2 - Tone Stability:');
console.log('Input:', input2);
console.log('Output:', output2);
 
const result2 = await metric.measure(input2, output2);
console.log('Metric Result:', {
  score: result2.score,
  info: result2.info,
});
// Example Output:
// Metric Result: {
//   score: 0.9444444444444444,
//   info: {
//     avgSentiment: 1.3333333333333333,
//     sentimentVariance: 0.05555555555555556
//   }
// }

Mixed Tone Example

Evaluate texts with varying sentiment:

src/index.ts


const input3 = 'The interface is frustrating and confusing, though it has potential.';
const output3 = 'The design shows promise but needs significant improvements to be usable.';
 
console.log('Example 3 - Mixed Tone:');
console.log('Input:', input3);
console.log('Output:', output3);
 
const result3 = await metric.measure(input3, output3);
console.log('Metric Result:', {
  score: result3.score,
  info: result3.info,
});
// Example Output:
// Metric Result: {
//   score: 0.4181818181818182,
//   info: {
//     responseSentiment: -0.4,
//     referenceSentiment: 0.18181818181818182,
//     difference: 0.5818181818181818
//   }
// }

Understanding the Results ✅

The metric provides different outputs based on the mode:

Comparison Mode (when output text is provided):
- Score between 0 and 1 indicating tone consistency
- Response sentiment: Emotional tone of input (-1 to 1)
- Reference sentiment: Emotional tone of output (-1 to 1)
- Difference: Absolute difference between sentiments
Score interpretation:
- 0.8-1.0: Very consistent tone
- 0.6-0.7: Generally consistent
- 0.4-0.5: Mixed tone
- 0.0-0.3: Conflicting tone
Stability Mode (when analyzing single text):
- Score between 0 and 1 indicating internal consistency
- Average sentiment: Overall emotional tone
- Sentiment variance: How much tone varies between sentences
Score interpretation:
- 0.9-1.0: Very stable tone
- 0.7-0.8: Mostly stable
- 0.4-0.6: Variable tone
- 0.0-0.3: Highly inconsistent

View Example on GitHub