Standing watch on the words that matter.
Track the keywords, handles and phrases a case depends on across six platforms — and see how each named person is being spoken about, not just whether a post was positive.
Six platforms, one keyword set.
No official platform APIs, and no logged-in accounts — so there is no application to be rejected and no account to be banned mid-operation.
Six platforms from one keyword set — no official platform APIs, and no logged-in accounts.
Coverage of platforms without a public API depends on third-party public mirrors and is best-effort. We scope exactly what is achievable for your keywords in a briefing.
Platform names and logos are trademarks of their respective owners. Their presence here identifies the sources CLERINT can lawfully collect from, and does not imply any partnership, endorsement or affiliation.
Sentiment toward each person named — not toward the post.
A post can praise one figure while attacking another. Post-level polarity averages that into nothing useful. Social Pulse scores every named entity separately, so a monitor tells you how each subject is being talked about rather than how the post feels overall.
- Per-entity scoring
- Each person, party or organisation in a post carries its own sentiment chip.
- Language identification
- 176 languages recognised, so a monitor is never silently English-only.
- Key phrases
- Surfaces what a burst of posts is actually about, without you having to read all of it.
- Post-level sentiment
- Scored locally on every post that passes the filter — nothing is sent out to be read.
Whole-word matching, so near-misses never enter the dataset.
A monitor is a structured query, not a bag of words. Terms combine, exclusions apply after collection, and matching is whole-word — so tracking Ata Tarar never fills the dataset with posts about Qatar.
It reads what the video says, and what is written on it.
A short-form video carries its message in speech and in on-screen text, and neither is in the caption. One click transcribes and reads both, then two models independently summarise what it means.
- Transcription
- Multilingual speech-to-text, with Urdu, Hindi and English handled directly.
- On-screen text
- Read from sampled frames, catching captions burned into the video that no caption field carries.
- Dual-model summary
- Two models run in parallel and both answers are kept, so disagreement is visible rather than hidden.
Built for someone reading it on a phone.
Social Pulse is in early access. Coverage of platforms without a public API depends on third-party public mirrors and is best-effort; we scope what is achievable for your specific keywords before you commit.
The things people ask before a briefing.
Which platforms does it monitor?
X, Bluesky, Mastodon, Threads, Instagram and TikTok, from one keyword set — with no official platform APIs and no logged-in accounts, so there is no application to be rejected and no account to be banned.
How is this different from ordinary sentiment analysis?
It scores sentiment toward each entity named in a post rather than the post as a whole. A post praising one figure while attacking another produces two different readings, not one meaningless average.
Does it read video?
Yes, on demand. A video is transcribed with Whisper — multilingual, including Urdu — and its on-screen text is read with OCR, then summarised. Both carry meaning the caption usually does not.
Is it generally available?
It is in early access. Coverage of platforms without a public API depends on third-party public mirrors and is best-effort, so we scope what is achievable for your specific keywords before you commit.
Tell us what you need to watch.
Social Pulse is in early access. Bring the keywords and handles that matter to you and we will scope exactly what is achievable across each platform before you commit to anything.