Social listening into sentiment reports
Brand mentions scatter across Twitter, Reddit, LinkedIn, Instagram, Facebook, with no single lens. Define a study, pull mentions per keyword, classify sentiment and feature requests, render a report.
Brand mentions scatter across Twitter, Reddit, LinkedIn, Instagram, Facebook, with no single lens.
Define a study, pull mentions per keyword, classify sentiment and feature requests, render a report.
The story
Every brand I’ve worked with has the same blind spot. People are already talking about them on Reddit, LinkedIn threads, Instagram comments, Twitter replies. Nobody on the team has a single place to see it. Marketing watches one corner, support watches another, product sees none of it.
I didn’t want a live dashboard that nobody opens. I wanted a snapshot you can share: the kind of thing you drop into a Monday standup or a board update and actually read top to bottom.
So I built RepReport. You describe the brand once, it goes and listens, and hands back a report.
What it does
You create a study in a 3-step wizard: company name, platforms to watch, and what you care about (praise, complaints, feature requests). RepReport pulls real mentions and classifies each one with a structured-output LLM call so the sentiment bucket is deterministic. Then a second pass reads the top mentions and their comment threads to write the narrative summary, bilingual EN/ES, with citation links baked into the prose back to the original threads.
The output is a single report page per study:
- Sentiment overview: breakdown of positive, negative, and neutral across the pulled mentions.
- Platform distribution: where the conversation is actually happening, not where you assumed it was.
- AI summary: a few paragraphs of analyst-tone prose with inline links to the specific mentions it’s citing.
- Raw mentions: every post and comment the study ingested, scored, linked, and searchable.