> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gaussia.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Humanity

> Measure emotional profiling and entropy of AI assistant responses using NRC emotion lexicons

## Overview

The **Humanity** metric analyzes the emotional profile of assistant responses using the NRC Emotion Lexicon. It computes emotion distributions, emotional entropy, and Spearman correlation against ground truth responses.

## Dimensions

For each interaction, the metric computes distribution scores across eight emotions:

| Emotion      | Description                           |
| ------------ | ------------------------------------- |
| Anger        | Frustration or hostility expressions  |
| Anticipation | Forward-looking or expectant language |
| Disgust      | Aversion or repulsion indicators      |
| Fear         | Anxiety or threat-related language    |
| Joy          | Positive or happy expressions         |
| Sadness      | Sorrow or melancholy indicators       |
| Surprise     | Unexpected or astonishing content     |
| Trust        | Reliability and confidence markers    |

Additionally, it computes:

* **Emotional entropy**: How diverse the emotional range is (higher = more diverse)
* **Ground truth Spearman correlation**: How closely the emotional profile matches the expected response

## Usage

```python theme={null}
from gaussia.metrics.humanity import Humanity

results = Humanity.run(MyRetriever)

for r in results:
    print(f"QA: {r.qa_id}")
    print(f"Emotional entropy: {r.humanity_assistant_emotional_entropy:.3f}")
    print(f"Spearman correlation: {r.humanity_ground_truth_spearman:.3f}")
    print(f"Joy: {r.humanity_assistant_joy:.3f}")
```

## Parameters

| Parameter   | Type              | Default    | Description     |
| ----------- | ----------------- | ---------- | --------------- |
| `retriever` | `type[Retriever]` | *required* | Retriever class |

<Note>
  The Humanity metric does **not** require an LLM — it uses lexicon-based analysis.
</Note>

## Output schema

### HumanityMetric

One result per interaction (not per session):

| Field                                  | Type    | Description                                   |
| -------------------------------------- | ------- | --------------------------------------------- |
| `session_id`                           | `str`   | Session identifier                            |
| `qa_id`                                | `str`   | Interaction identifier                        |
| `assistant_id`                         | `str`   | Assistant identifier                          |
| `humanity_assistant_emotional_entropy` | `float` | Emotional diversity (Shannon entropy)         |
| `humanity_ground_truth_spearman`       | `float` | Correlation with ground truth emotion profile |
| `humanity_assistant_anger`             | `float` | Anger score (0–1)                             |
| `humanity_assistant_anticipation`      | `float` | Anticipation score (0–1)                      |
| `humanity_assistant_disgust`           | `float` | Disgust score (0–1)                           |
| `humanity_assistant_fear`              | `float` | Fear score (0–1)                              |
| `humanity_assistant_joy`               | `float` | Joy score (0–1)                               |
| `humanity_assistant_sadness`           | `float` | Sadness score (0–1)                           |
| `humanity_assistant_surprise`          | `float` | Surprise score (0–1)                          |
| `humanity_assistant_trust`             | `float` | Trust score (0–1)                             |

<Note>
  Requires the `humanity` extra: `pip install "gaussia[humanity]"`.
</Note>