# Sentiment

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()

```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/sentiment.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.