Retrieving comments | Siftsy Analyst API

GET /posts/{postId}/analysis/comments and GET /analyses/{analysisId}/comments return a sample of comments behind the analysis — the evidence for the scores and mix bars, not the whole thread.

Siftsy is an analysis platform, not a comment exporter. These routes are not a way to dump full comment sections into a warehouse or CRM. Ask for a sample (source=notables, content, or all), optionally narrowed to a mix-bar bucket. Prefer the analysis numbers themselves when you need totals or mix.

The sample uses the same five-bucket keys as the analysis mix bars and GET /posts/{postId}/metrics. Mix-bar filters below narrow that sample.

Values within one parameter combine with OR. Different parameters combine with AND. Unscored comments are excluded when you filter that dimension. Active filters are echoed in meta.filters.

Filter	What it does	Example
`sentiment`	Overall sentiment bucket	`sentiment=positive`
`content`	Content sentiment (alignment with the post / creator)	`content=positive,lean_positive`
`topic`	Topic sentiment (reception of the subject)	`topic=negative`
`relevance`	How on-topic the comment is	`relevance=on_topic`
`consensus`	How the commenter is reacting to other commenters	`consensus=fighting`

Canonical keys on every dimension: positive, lean_positive, neutral, lean_negative, negative.

Relevance and consensus also accept the dashboard labels:

Key	Sentiment	Relevance	Consensus
`positive`	Positive	`on_topic`	`agreeing`
`lean_positive`	Lean positive	`relevant`	`discussing`
`neutral`	Neutral	—	—
`lean_negative`	Lean negative	`general`	`debating`
`negative`	Negative	`off_topic`	`fighting`

neutral is typically unused for relevance and consensus. Score version 3 overall sentiment has a true unused middle band (4.5–5.5); v1/v2 overall sentiment does not. Content and topic always use the five-bucket split. See Scores for numeric cutoffs.

Consensus is not agreement with the post. consensus=fighting is commenters arguing with each other. A thread can be uniformly negative toward the post and still have high consensus.

Examples

Comments in the positive overall-sentiment bar:

curl -sS -H "Authorization: Bearer YOUR_API_KEY" \
  "https://api.siftsy.app/analyst/v1/posts/YOUR_POST_ID/analysis/comments?sentiment=positive"

On-topic comments that are fighting with other commenters:

curl -sS -H "Authorization: Bearer YOUR_API_KEY" \
  "https://api.siftsy.app/analyst/v1/posts/YOUR_POST_ID/analysis/comments?relevance=on_topic&consensus=fighting"

Content-positive and topic-negative (split reaction: aligned with the creator, hostile to the subject):

curl -sS -H "Authorization: Bearer YOUR_API_KEY" \
  "https://api.siftsy.app/analyst/v1/posts/YOUR_POST_ID/analysis/comments?content=positive&topic=negative"

Each comment in the response includes scores (1–10) and buckets (the mix-bar key), so you can confirm the filter matched the values you got back. Invalid bucket names return 400.

Pass source= to choose the sample:

`source`	What you get
`notables`	Highest-engagement, most relevant comments (the usual evidence set)
`content`	Comments grouped onto the creator’s topics and messages
`all`	The union of those two

Sampled rows include sampleReasons. meta.groups lists creator topics and messages with commentCount and sampleCommentIds. See Post analysis. The same filters work on GET /analyses/{analysisId}/comments.

Filtering at analysis time

The query-string filters above narrow the sample after the analysis already exists. To make the analysis itself describe a narrowed set — so scores, mix bars, and share-of-comments all reflect it — pass filters to POST /analyses. That accepts the same bucket names plus the rest of the dashboard controls: keywords, usernames, creator comments, replies, questions, mentions, emoji-only comments, a likes floor, and a date range. GET /analyses/{analysisId}/comments then applies the analysis’s filters first and samples from that matched set.