surface↓analysis↓sentiment_scalar
Analysisscalar · returns double

SENTIMENT_SCALAR

Per-row sentiment score (-1.0 to 1.0) for a text value

Per-row — runs once for each row.

analysishybridspecialist-zooscales-largetext

Syntax

semantic_sentiment_scalar({{ text }})
SENTIMENT_SCALAR({{ text }})

Arguments

nametypedescription
textVARCHAR—

About

Scalar sentiment analysis — returns a numeric sentiment score from -1.0 to 1.0 for a single text, per-row. Backend: specialist zoo (twitter-roberta-base-sentiment-latest via TEI). Each row is a single sub-millisecond GPU call, batched transparently by TEI under load. Call as: SELECT SENTIMENT_SCALAR(review) FROM reviews -- recommended (unambiguous) SELECT SENTIMENT(review) FROM reviews -- shorter alias; may collide with the DIMENSION operator of the same name — prefer the _SCALAR form to be explicit. RELATED OPERATORS (same root name, different shapes): • sentiment (DIMENSION) use in GROUP BY to auto-cluster rows into labeled sentiment buckets e.g. GROUP BY sentiment(review) • semantic_sentiment (AGGREGATE) use SENTIMENT_AGG(col) to collapse a collection of texts into a single overall score (-1.0 to 1.0)

Examples

Positive sentiment returns positive score

SELECT
  semantic_sentiment_scalar ('I absolutely love this!')

Negative sentiment returns negative score

SELECT
  semantic_sentiment_scalar ('This is terrible and I hate it')

Nearby rabbit holes

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AB_TEST

Bayesian A/B test (Beta-Binomial) returning full posterior + recommendation

analysisdeterministicnumeric
scalar

ASSESS_CONFIDENCE

Assess the confidence/quality of a cascade execution result.

analysisllmtext
aggregate

COMPARE

Compare values in a group for similarities, differences, and patterns

analysisllmtext
scalar

COUNTERARGUMENT

Generate the strongest counterargument to a position

analysisllmtext
scalar

DP_COUNT

Differentially-private count (Laplace mechanism, sensitivity=1)

analysisdeterministicjson
scalar

DP_MEAN

Differentially-private mean (Laplace mechanism)

analysisdeterministicjson
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