Scores | Siftsy Analyst API

The Siftsy Score is a 1–10 temperature check for a comment section: how people reacted to the content. It is not a like count and not a generic “positive or negative” label. It blends three independent readings, each scored by context-aware AI on the same 1–10 scale:

Sentiment — how commenters feel about the post and the subjects it discusses
Relevance — how on-topic the conversation actually is
Consensus — how commenters are reacting to each other

Sentiment carries the most weight. Relevance and Consensus keep a high score honest: a cheerful but off-topic thread, or a pile-on fight, should not read as a clean win.

Every number you get from the Siftsy Analyst API is already stored. The endpoints never rescore. Read GET /team → analysisSettings.scoreVersion (and engagementWeight) so you interpret the rollup the same way the dashboard does.

Consensus is not agreement with the post. Sentiment (and Content / Topic) is how commenters feel about the post, creator, or subject. Consensus is how they treat other commenters. A thread can be uniformly negative toward the post and still have high consensus.

Content vs. topic

A single sentiment number collapses two different stories: “I don’t agree with this video” and “I hate this subject.” On score version 2 and 3, Sentiment splits into two readings on every comment, then averages them 50/50 into overall sentiment:

Score	Asks	High means	Low means
Content (`content`)	How do they feel about this post and its creator?	Backing / echoing the publisher	Pushback against the post or creator
Topic (`topic`)	How do they feel about the subjects being discussed?	Enthusiastic about the subject	Hostile toward the subject
Sentiment (`sentiment`)	Both, combined	The section landed	The section did not

The average is compatible with older versions. The split is the diagnosis.

A high Content score with a low Topic score means the audience is with the creator and against the subject — the execution worked; the message or the thing being discussed did not. The reverse means the subject resonates and the post is what is missing. Own-social teams usually read Content first. PR and crisis teams usually read Topic first. The underlying scores do not change when you switch that lens; only which story you lead with does.

Relevance and consensus

These are supporting measures, not a second sentiment score.

Relevance is whether the comment is actually about the post, the creator, or what the post is talking about. High is a specific, on-topic reply. Low is spam, solicitation, or a comment that could sit under anything. A relevant criticism still scores high on Relevance.

Consensus is commenter-to-commenter. High is agreeing with or echoing other comments. Low is arguing with them — debate, contradiction, fighting. Never treat “agrees with the post” as high Consensus or “disagrees with the post” as low Consensus.

Score	Measures
Sentiment	How commenters feel about the post and the subjects it discusses
Content	Alignment with the post / creator (score version 2+)
Topic	Reception of the subject itself (score version 2+)
Relevance	How on-topic the comment is
Consensus	Alignment among commenters, not with the post

How they become a Siftsy Score

Per-comment scores are the same across versions. A version is a different way of rolling those comments up — switching versions on the team recalculates instantly and does not rescore.

	Version 1	Version 2	Version 3
Blend	Sentiment, Relevance, and Consensus equally (1/3 each)	Sentiment 66%, Relevance 16.5%, Consensus 16.5%	Same weights as v2
Sentiment	One reading per comment	Same as v1	Content + Topic, averaged 50/50
Reading	Immaculate 7.0+ · Good 5.0–6.9 · Sub-par 3.0–4.9 · Off below 3	Same as v1	Immaculate 7.0+ · Mixed 4.5–6.9 · Off below 4.5

Version 3 also hardens the rollup against the ways comment sections mislead an average:

Negativity cap. If a meaningful share of comments are very negative (below 3), the score’s ceiling drops. A section that is about 12% very negative can score at most 8.5. A loud positive majority cannot paper over a hostile contingent.
Engagement weighting. Comments with more likes and replies count more on a curve: every comment keeps at least 1× weight and the most-engaged comment counts up to 5×. That multiplier applies to the sentiment readings only. Relevance and Consensus describe the comments themselves, so likes do not scale them.
Reply hierarchy. Top-level comments carry full weight. Replies share a capped pool, so a long argument under one comment does not outweigh the rest of the thread.

scores.siftsyScore is that rollup. scores.sentiment, content, topic, relevance, and consensus are the components.

Mix buckets

GET /posts/{postId}/metrics (and the mix object on an analysis) returns five-bucket percentages for each dimension. The JSON keys are the same; the dashboard labels change with the score.

Key	Sentiment	Content	Topic	Relevance	Consensus
`positivePercent`	Positive	Supportive	Enthusiastic	On-topic	Agreeing
`leanPositivePercent`	Lean positive	Receptive	Favorable	Relevant	Discussing
`neutralPercent`	Neutral	Split	Mixed	—	—
`leanNegativePercent`	Lean negative	Skeptical	Critical	General	Debating
`negativePercent`	Negative	Opposed	Hostile	Off-topic	Fighting

neutralPercent is typically unused for Relevance and Consensus. Filter comments with the dashboard labels or the JSON keys — both work. See Retrieving comments.

Numeric cutoffs

These are the mix-bar ranges. A comment in a bucket is the same comment counted in that bar on GET /posts/{postId}/metrics and GET /posts/{postId}/analysis, and the same comment that can appear in a comments sample filtered by ?sentiment=… (or content, topic, relevance, consensus).

Key	Five-bucket (v3 overall sentiment; always for content, topic, relevance, consensus)	v1/v2 overall sentiment
`positive`	≥ 7	≥ 7
`lean_positive`	> 5.5 and < 7	≥ 5 and < 7
`neutral`	4.5–5.5	unused
`lean_negative`	≥ 3 and < 4.5	≥ 3 and < 5
`negative`	< 3	< 3

Metrics are materialized. You get 404 with metrics_not_available until scoring has finished.