Mirror of <https://developer.salesforce.com/docs/data/data-cloud-query-guide/references/dc-sql-reference/ai-sentiment.html> (Salesforce, Data 360 Query Guide), read 2026-10-23. Salesforce's own page is the authority; this copy exists because that site refuses non-browser readers.

`AI_SENTIMENT`

Applies to: ✅ Data 360 SQL ❌ Tableau Hyper API

Performs sentiment analysis on text input and returns a sentiment label. This function uses a multilingual sentiment model to analyze customer feedback, support tickets, survey responses, and other unstructured text.

input_text: The text to analyze for sentiment. The function processes empty input values normally. The text must be a non-NULL value.

Returns a text sentiment label. Sentiment labels are:

Positive: Text expressing positive sentiment
Negative: Text expressing negative sentiment
Neutral: Text expressing neutral sentiment
Mixed: Text containing conflicting positive and negative signals
Unknown: Text with unclear or indeterminate sentiment

Input text length: The function supports input text up to 512 tokens (approximately 400-500 words). For longer text such as full call transcripts or lengthy emails, split the content into shorter passages before analyzing.
Best for short text: This function works best on short, intent-rich text such as support tickets, CRM notes, social media posts, and chat messages.
Performance: Requests to the function time out after 30 seconds.
First execution: When executing this function on a large number of rows for the first time, you can encounter timeout or capacity errors. Start with smaller row counts and gradually increase volume to avoid these issues.
Non-NULL values: input_text must be a non-NULL value. Add IS NOT NULL to the WHERE clause in your SQL statement to avoid NULL value errors.
Multilingual support: The underlying model supports multiple languages.

Analyzes sentiment for multiple rows from a table.

Returns the sentiment of the feedback for each row in the AutomobileServiceFeedback__dll.

Show only positive feedback in a table.

Returns all positive feedback.

Sentiment Analysis: Learn about sentiment analysis models, use cases, and integration options.
AI Models in Data Cloud: Read an overview of AI model capabilities.