How Fit Score, intent tags and sentiment work
What Fit Score, intent tags and sentiment mean on each mention, how the High, Medium and Low bands are set, and how to tune scoring for your listeners.
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Every new mention a listener finds is analyzed by AI before it reaches your feeds. The analysis adds a short summary, a Fit Score, up to three intent tags, a sentiment score and the detected language. Scoring one mention costs 0.5 credits (see Credits and usage).
Fit Score
Fit Score answers one question: is this the kind of post you actually care about? It is shown as a percentage next to each mention. Hover it in the mention panel to see the in-app definition: a weighted score based on your Brand Context, listener intent and mention content.
Fit Score bands
| Band | Fit Score |
|---|---|
| High | 70% and above |
| Medium | 50% to 69% |
| Low | Below 50% |
The Fit score filter on feeds, analytics reports and notifications uses these three bands.
What drives the score
Fit Score has two parts.
- Relevance. The AI reads the mention (and the author's headline or role on LinkedIn, when available) next to your workspace Brand Context: company description, product summary and positioning, use cases, target audiences and ideal customer profiles. It also reads the listener's intent signal, noisy signals and keywords. A mention that clearly matches the noisy signals scores very low even when it contains your keywords.
- Intent weights. When you save a listener's intent signal, SnitchFeed gives every intent tag a weight from 0 to 5 for that listener, based on your intent signal and noisy signals. A tag you care about gets a high weight; a tag you want to avoid gets 0.
When a mention has intent tags and the listener has weights, Fit Score is 60% relevance and 40% weighted intent tags. When a mention has no intent tags, Fit Score equals relevance.
Where Fit Score is used
- All mentions opens with Fit score set to Medium and High and sorted by Fit Score (high to low). See Feeds.
- Opportunities lists only High and Medium mentions.
- A feed's Fit score filter also decides which mentions its notifications send.
- The Dashboard reports the share of Low mentions as Noise Filtered.
Intent tags
Intent tags are AI labels on each mention that describe what the author is doing. A mention gets up to three tags, or none when nothing clearly applies. Do not confuse them with the intent signal, which is the text you write on a listener.
| Tag | Meaning |
|---|---|
| Buying Intent | The author is evaluating, researching, budgeting for or searching for a product, service or vendor. |
| Recommendation Request | The author asks others to recommend tools, vendors or solutions. |
| Comparison | The author compares products, tools or approaches to make a decision. |
| Seeking Alternative | The author is unhappy with a current solution and wants to replace it. |
| Content Opportunity | A question or discussion that could inspire educational, SEO or social content. |
| Pain Point | The author describes frustration, blockers or inefficiency. |
| Competitor Complaint | The author criticizes a competing product or company. |
| Competitor Mention | A competitor is named or discussed, with or without a clear opinion. |
| Feature Request | The author asks for missing functionality, integrations or improvements. |
| Praise | The author gives positive feedback or an endorsement. |
| Hiring | The mention is about hiring, recruiting or job seeking. |
| Brand Mention | The mention names your own brand, product, company or domain. |
| Promotional | The author is promoting or marketing a product, offer or themselves. |
Use Include Intents and Exclude Intents in the feed filters to keep or hide tags, or break an analytics report down by Intent tags.
Sentiment
Sentiment is the tone of the post, scored from 0 to 1 and grouped into three buckets:
| Bucket | Sentiment score |
|---|---|
| Positive | 0.6 and above |
| Neutral | 0.4 to just below 0.6 |
| Negative | Below 0.4 |
Sentiment is separate from Fit Score. A competitor complaint can be negative and still be a High Fit Score lead.
Tune scoring
Re-scoring costs credits
When you change a listener's intent signal or noisy signals, SnitchFeed re-scores that listener's most recent mentions in the background. Each re-scored mention costs 0.5 credits, the same as a new one.
- Complete your Brand Context. Scoring compares every mention against it. Gaps in your ICPs or use cases make scores less precise. See Brand Context.
- Write a specific intent signal. Describe the people and situations you want to find in 200 to 500 characters. See Write an intent signal.
- Add noisy signals. Describe the posts that keep showing up but are not useful, such as job posts or vendor promotions.
- Check weak keywords. The Dashboard card Worst Performing Terms shows the keywords with the highest share of Low mentions over the last two weeks.
- Leave feedback. Show more like this and Show less like this on a mention record your feedback with a reason. They do not change that mention's score or archive it.
Related
- Feeds: filter, save and review mentions: filter by Fit Score, intent tags and sentiment.
- Write an intent signal for better Fit Scores: the listener field that calibrates scoring.
- Brand Context: how AI learns your business: the workspace profile every score is based on.
- Too few high Fit Score mentions: fixes: what to change when High mentions are rare.
- MCP concepts: the same fields as AI assistants see them.
Brand Context: how AI learns your business
Brand Context is your workspace profile: company, audience, ICPs, use cases and case studies. SnitchFeed builds it from your website to score mentions.
What is a listener in SnitchFeed?
A listener holds the keywords, tracked profiles, intent signal and scan settings that decide which posts SnitchFeed collects and scores for you.