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String & binary functions (AI Functions)

AI_SENTIMENT¶

AI_SENTIMENT provides sentiment classification across diverse markets and languages. It uses a custom Snowflake large language model to return overall sentiment for a piece of text and, optionally, aspect-based (also called category-based or entity-based) sentiment for specific topics that you specify.

Common use cases include:

• Social media monitoring
• Detailed product analysis
• Comprehensive brand perception studies
• Advanced market intelligence
• Employee engagement analysis
• Customer experience journey mapping
• Content performance analysis
• Customer support optimization

Syntax¶

AI_SENTIMENT( <text> [ , <categories> ] [, <return_error_details> ] )

Arguments¶

Required:

text

A string containing the text in which sentiment is detected.

Optional:

categories

An array containing up to ten categories (also called entities or aspects) for which sentiment should be extracted. Each category is a string. For example, if extracting sentiment from a restaurant review, you might specify ['cost', 'quality', 'service', 'wait time'] as the categories. Each category may be a maximum of 30 characters long.

If you do not provide this argument, AI_SENTIMENT returns only the overall sentiment.

return_error_details

A BOOLEAN flag that indicates whether to return error details in case of error. When set to TRUE, the function returns an OBJECT that contains the value and the error message, one of which is NULL depending on whether the function succeeded or failed. See Error behavior for details.

Returns¶

An OBJECT value containing a categories field. categories is an array of category records. Each category includes these fields:

• name: The name of the category. The category names match the categories specified in the categories argument.
• sentiment: The sentiment of the category. Each sentiment result is one of the following strings.
  • unknown: The category was not mentioned in the text.
  • positive: The category was mentioned positively in the text.
  • negative: The category was mentioned negatively in the text.
  • neutral: The category was mentioned in the text, but neither positively nor negatively.
  • mixed: The category was mentioned both positively and negatively in the text.

The overall category record is always included and contains the overall sentiment of the text.

Example:

{
  "categories": [
    {
      "name": "overall",
      "sentiment": "mixed"
    },
    {
      "name": "Brand",
      "sentiment": "unknown"
    },
    {
      "name": "Cost",
      "sentiment": "negative"
    },
    {
      "name": "Professionalism",
      "sentiment": "unknown"
    }
  ]
}

Error behavior¶

By default, if AI_SENTIMENT can’t process the input, the function returns NULL. If the query processes multiple rows, rows with errors return NULL and don’t prevent the query from completing.

The return value on error depends on the return_error_details argument. The following table shows the return value based on the return_error_details argument:

return_error_details	Return value	Description
FALSE Not passed	NULL
TRUE	OBJECT with value and error fields	value: An OBJECT containing the sentiment analysis result, or NULL if an error occurred. error: A VARCHAR value that contains the error message if an error occurred, or NULL if the function succeeded.

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For more information about error handling for AI functions, see Snowflake Cortex AI Function: Multirow error handling improvements.

Access control requirements¶

Users must use a role that has been granted the SNOWFLAKE.CORTEX_USER database role. See Cortex LLM privileges for more information on this role.

Usage notes¶

• Supported languages: AI_SENTIMENT can analyze sentiment in English, French, German, Hindi, Italian, Spanish, and Portuguese. Aspects can be specified in the language of the text or in English.
• Context window: AI_SENTIMENT is optimized for text inputs of 2,048 tokens (roughly 1,600 words). Inputs that exceed the context window return an error; outputs that would exceed the context window are truncated.
• Aspect limits: You can specify a maximum of ten aspects, each no longer than 30 characters.

Examples¶

Get the overall sentiment of text¶

The following example uses AI_SENTIMENT to get the overall sentiment of a product review.

SELECT AI_SENTIMENT('I went to the store, bought the leggings and exact same as shorts...
  they are expensive but i heard such great things. After wearing them twice i noticed a string popping out already.
  And since i believed that they were this amazing luxury brand i didnt keep the receipt 😭 ');

Return value:

{
  "categories": [
    {
      "name": "overall",
      "sentiment": "mixed"
    }
  ]
}

Get aspect-based sentiment¶

Specify one or more aspects to get aspect-based sentiment in addition to the overall sentiment. The following example uses AI_SENTIMENT to get the sentiment classification for specific aspects of a restaurant review.

SELECT AI_SENTIMENT('A tourist\'s delight, in low urban light,
  Recommended gem, a pizza night sight. Swift arrival, a pleasure so right,
  Yet, pockets felt lighter, a slight pricey bite. 💰🍕🚀',
  ['Cost', 'Quality', 'Wait Time']);

Return value:

{
  "categories": [
    {
      "name": "overall",
      "sentiment": "mixed"
    },
    {
      "name": "Cost",
      "sentiment": "negative"
    },
    {
      "name": "Quality",
      "sentiment": "positive"
    },
    {
      "name": "Wait Time",
      "sentiment": "positive"
    }
  ]
}

If some aspects that you specify do not apply to the text you provide, AI_SENTIMENT returns unknown for those aspects, as shown for Professionalism and Brand in the following example.

SELECT AI_SENTIMENT('A tourist\'s delight, in low urban light,
  Recommended gem, a pizza night sight. Swift arrival, a pleasure so right,
  Yet, pockets felt lighter, a slight pricey bite. 💰🍕🚀',
  ['Cost', 'Professionalism', 'Brand']);

Return value:

{
  "categories": [
    {
      "name": "overall",
      "sentiment": "mixed"
    },
    {
      "name": "Brand",
      "sentiment": "unknown"
    },
    {
      "name": "Cost",
      "sentiment": "negative"
    },
    {
      "name": "Professionalism",
      "sentiment": "unknown"
    }
  ]
}

Analyze multiple reviews from a table¶

In this example, a table named reviews contains a column named review_content containing the text of movie reviews submitted by users. The query returns the sentiment of several facets of up to ten reviews.

SELECT
  AI_SENTIMENT(
    review_content,
    ['concept', 'performance', 'script', 'cinematography', 'soundtrack']
  ),
  review_content
  FROM reviews LIMIT 10;

Multilingual sentiment analysis¶

AI_SENTIMENT can analyze sentiment in multiple languages, so you don’t need to translate the text and risk losing an essential part of its meaning. You do not need to specify the language of the text. Aspects can be specified in the language of the text or in English.

The following example has both text and labels in Spanish:

SELECT AI_SENTIMENT ('Pedí dos pares del mismo modelo en diferentes colores.
    Uno tenía defectos en la costura y el cuero se veía de menor calidad.
    Por 350€ el par, esto es inaceptable. El servicio al cliente tardó una
    semana en responder y la solución no fue satisfactoria. Es una pena porque
    cuando están bien hechos, son zapatos hermosos. Pero la inconsistencia en la
    calidad es preocupante.',
    ['Calidad', 'Calidad de Servicio', 'Precio', 'Tiempo de Espera']);

Return value:

{
  "categories": [
    {
      "name": "overall",
      "sentiment": "negative"
    },
    {
      "name": "Calidad",
      "sentiment": "negative"
    },
    {
      "name": "Calidad de Servicio",
      "sentiment": "negative"
    },
    {
      "name": "Precio",
      "sentiment": "negative"
    },
    {
      "name": "Tiempo de Espera",
      "sentiment": "negative"
    }
  ]
}

The following example has text in German and labels in English:

SELECT AI_SENTIMENT ('Die Schuhe selbst sind wirklich schön und gut verarbeitet.
    Das Leder ist weich und die Passform stimmt. Allerdings gab es erhebliche
    Verzögerungen bei der Lieferung - statt der versprochenen 5 Tage hat es 3
    Wochen gedauert. Der Kundenservice war freundlich, aber nicht sehr hilfreich.
    Für 320€ erwarte ich besseren Service. Die Schuhe sind in Ordnung, aber das
    Gesamterlebnis war mittelmäßig',
    ['Quality', 'Price', 'Service', 'WaitTime']);

Return value:

{
  "categories": [
    {
      "name": "overall",
      "sentiment": "mixed"
    },
    {
      "name": "Price",
      "sentiment": "neutral"
    },
    {
      "name": "Quality",
      "sentiment": "positive"
    },
    {
      "name": "Service",
      "sentiment": "neutral"
    },
    {
      "name": "WaitTime",
      "sentiment": "negative"
    }
  ]
}

Regional availability¶

AI_SENTIMENT is available in the following regions:

Legal notices¶