AI_SENTIMENT classifies the sentiment of a text column using a Large Language Model (LLM) and returns a score, label, and confidence value for each row.
Limitations
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Requires Ververica Runtime (VVR) 11.4 or later.
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Throughput is bounded by the rate limit of the model platform. When the rate limit is exceeded, the Flink operator experiences backpressure and becomes a bottleneck. In severe cases, this triggers operator timeout errors and causes the job to restart.
Syntax
AI_SENTIMENT(
MODEL => MODEL <model_name>,
INPUT => <input_column>
)Input parameters
| Parameter | Data type | Description |
|---|---|---|
MODEL <model_name> |
MODEL | The registered model service to use. Register a model service in Model Settings. The model's output type must be VARIANT. |
<input_column> |
STRING | The text column to analyze. |
Outputs
AI_SENTIMENT returns one row per input row with the following columns:
| Column | Data type | Description |
|---|---|---|
score |
DOUBLE | Sentiment score from -1.0 to 1.0. Reference values: -1.0 extremely negative, -0.5 moderately negative, 0.0 neutral, 0.5 moderately positive, 1.0 extremely positive. |
label |
STRING | Sentiment classification. Valid values: positive, negative, neutral. |
confidence |
DOUBLE | Model confidence in the predicted label, between 0.0 and 1.0. |
Example
The following example registers a Qwen-Plus model and uses AI_SENTIMENT to classify the sentiment of movie comments.
Test data
| id | movie_name | comment | actual_label |
|---|---|---|---|
| 1 | Good Stuff | I loved the part where the child guessed sounds. It was one of the most romantic narratives I've seen in a movie. Very gentle and full of love. | positive |
| 2 | Dumpling Queen | Nothing remarkable. | negative |
SQL statement
-- Register the model service
CREATE TEMPORARY MODEL general_model
INPUT (`input` STRING)
OUTPUT (`content` VARIANT)
WITH (
'provider' = 'openai-compat',
'task' = 'chat/completions',
'model' = 'qwen-plus'
);
-- Create a view with the test data
CREATE TEMPORARY VIEW movie_comment(id, movie_name, user_comment, actual_label)
AS VALUES
(1, 'Good Stuff', 'I loved the part where the child guessed sounds. It was one of the most romantic narratives I\'ve seen in a movie. Very gentle and full of love.', 'positive'),
(2, 'Dumpling Queen', 'Nothing remarkable.', 'negative');
-- Call AI_SENTIMENT using positional arguments
SELECT id, movie_name, actual_label, score, label, confidence
FROM movie_comment,
LATERAL TABLE(
AI_SENTIMENT(MODEL general_model, user_comment));
-- Call AI_SENTIMENT using named arguments
SELECT id, movie_name, actual_label, score, label, confidence
FROM movie_comment,
LATERAL TABLE(
AI_SENTIMENT(
MODEL => MODEL general_model,
INPUT => user_comment));Replace the following placeholders with your actual values:
| Placeholder | Description |
|---|
Output
The predicted label matches the actual_label for both rows.
| id | movie_name | actual_label | score | label | confidence |
|---|---|---|---|---|---|
| 1 | Good Stuff | positive | 0.8 | positive | 0.95 |
| 2 | Dumpling Queen | negative | -1.0 | negative | 0.95 |