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

# AI Analyze Sentiment

export const ComponentMetadata = ({warehouses, unsupportedWarehouses = [], componentType, connectionInputs, connectionOutputs}) => {
  const allWarehouses = [...warehouses.map(w => ({
    name: w,
    supported: true
  })), ...unsupportedWarehouses.map(w => ({
    name: w,
    supported: false
  }))];
  return <div style={{
    background: 'var(--colors-background-light, #f9fafb)',
    border: '1px solid var(--colors-border-default, #e5e7eb)',
    borderRadius: '12px',
    padding: '20px 28px',
    marginBottom: '28px',
    boxShadow: '0 1px 4px rgba(0,0,0,0.10)'
  }}>
      <table style={{
    width: '100%',
    borderCollapse: 'collapse'
  }}>
        <tbody>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle',
    width: '180px'
  }}>Project Availability</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>
              <div style={{
    display: 'flex',
    flexWrap: 'wrap',
    gap: '8px'
  }}>
                {allWarehouses.map((w, i) => <span key={i} style={{
    background: w.supported ? '#dcfce7' : '#fee2e2',
    color: w.supported ? '#15803d' : '#b91c1c',
    border: `1px solid ${w.supported ? '#bbf7d0' : '#fca5a5'}`,
    borderRadius: '9999px',
    padding: '3px 12px',
    fontSize: '0.85rem',
    fontWeight: '500',
    whiteSpace: 'nowrap'
  }}>
                    {w.name} {w.supported ? '✅' : '❌'}
                  </span>)}
              </div>
            </td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Component Type</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>{componentType}</td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Connection Inputs</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>{connectionInputs}</td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Connection Outputs</td>
            <td style={{
    verticalAlign: 'middle'
  }}>{connectionOutputs}</td>
          </tr>
        </tbody>
      </table>
    </div>;
};

<ComponentMetadata warehouses={["Databricks"]} unsupportedWarehouses={["Snowflake", "Amazon Redshift", "BigQuery"]} componentType="Transformation" connectionInputs="One" connectionOutputs="Unlimited" />

<Info>
  Production use of this feature is available for specific editions only. [Contact our sales team](https://www.matillion.com/contact) for more information.
</Info>

The **AI Analyze Sentiment** transformation component uses the Databricks [ai\_analyze\_sentiment()](https://docs.databricks.com/en/sql/language-manual/functions/ai_analyze_sentiment.html) function to invoke generative AI to perform sentiment analysis on input text. This function uses a Databricks chat model serving endpoints made available by [Databricks Foundation Model APIs](https://docs.databricks.com/en/machine-learning/foundation-models/index.html).

<Note>
  Make sure you have read and understand the [Requirements](https://docs.databricks.com/en/sql/language-manual/functions/ai_analyze_sentiment.html#requirements) set out by Databricks before using this component.
</Note>

The returned sentiment score is a string that describes the sentiment with one of the following words:

* positive
* negative
* neutral
* mixed

If the sentiment can't be detected, `null` is returned.

### Use case

Sentiment analysis classifies the tone or emotional intent of text. This is especially valuable when working with unstructured text fields like customer feedback, reviews, support tickets, or social media content. The output can typically be used with components such as [Filter](/docs/components/filter) to isolate strongly negative responses, or [Aggregate](/docs/components/aggregate) to group sentiment by product, region, or customer.

Some typical uses of this component include:

* Automatically classify sentiment of product reviews, NPS responses, or survey comments, to identify which customers are unhappy and trigger follow-ups.
* Analyze social media posts to classify brand mentions as positive, negative, or neutral in real-time social feeds and understand brand sentiment.

***

## Properties

<ResponseField name="Name" type="string" required>
  A human-readable name for the component.
</ResponseField>

{/* <!-- param-start:[columns] | warehouses: [databricks] --> */}

<ResponseField name="Columns" type="dual listbox" required>
  Use the arrow buttons or use drag-and-drop to move columns to the right-hand listbox to analyze for sentiment. A new column is created for each column that is analyzed.
</ResponseField>

{/* <!-- param-start:[includeInputColumns] | warehouses: [databricks] --> */}

<ResponseField name="Include Input Columns" type="boolean" required>
  * **Yes:** Outputs both your source input columns *and* the sentiment score columns. This will also include those input columns *not* selected in **Columns**.
  * **No:** Only includes the new sentiment score columns.
</ResponseField>