> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/sentiment.md).

# Sentiment

Track sentiment in LLM outputs. Use guardrails to manage tone and align with audience expectations.

Sentiment Guardrail is used to detect a prompt's sentiment and that the prompt has a sentiment score lower than the threshold. The scanner based on the NLTK's SentimentIntensityAnalyzer.

**Parameters**:

`data:`

* `prompt` (str): The prompt to scan for sentiment.

`arguments:`

* `threshold` (float, optional): Threshold for the sentiment score (from -1 to 1). Default is -0.1.

**Interpretation**

The sentiment score is calculated using nltk's Vader sentiment analyzer. The SentimentIntensityAnalyzer produces a sentiment score ranging from -1 to 1:

-1 represents a completely negative sentiment.

0 represents a neutral sentiment.

1 represents a completely positive sentiment.

**Example:**

```
pos_sentiment = "The book was good."

neutral_sentiment = "The plot was good, but the characters are uncompelling and the dialog is not great."

neg_sentiment = "A really bad, horrible book."

threshold = -0.1
```

`Results:`

```
pos_sentiment -> Passed , Score = 0.44

neutral_sentiment -> Failed, Score = -0.70

neg_sentiment -> Failed = -0.82
```

#### Code Example:

```python
`
pos_sentiment = "The book was good."
neutral_sentiment = "The plot was good, but the characters are uncompelling and the dialog is not great."
neg_sentiment = "A really bad, horrible book."
evaluator.add_test(
    test_names=["sentiment_guardrail"],
    data={
        "prompt": pos_sentiment
    }
    ).run()

evaluator.print_results()

```