Earnings call sentiment analysis, across your whole universe
Earnings call sentiment analysis scores the tone of management's language - confidence, hedging, evasion - from the transcript itself. Done at scale, the useful signal is the gap between upbeat prepared remarks and a hedged Q&A, and the change versus last quarter. Cutonce scores both parts of every call and flags the biggest moves.
Earnings call sentiment analysis scores the tone of management's language - confidence, hedging, evasion - from the transcript itself. Done at scale, the useful signal is the gap between upbeat prepared remarks and a hedged Q&A, and the change versus last quarter. Cutonce scores both parts of every call and flags the biggest moves.
The pipeline
How to build it
- 1Score prepared remarks and Q&A separately
A single tone number for the whole call mostly measures how long the prepared section was. Scoring the two parts apart exposes the gap that matters.
- 2Choose a scoring method that fits
Lexicon methods are transparent and cheap; an LLM captures context and hedging. The pipeline lets you pick per run.
- 3Track the change, not just the level
A team that turns hedged this quarter after two upbeat ones is the signal. The pipeline diffs each call against the prior period.
- 4Flag the biggest movers
Filter to the names whose tone moved most, up or down, and deliver just those.
A daily or quarterly email of the names whose call sentiment moved most, with the prepared-vs-Q&A gap and the passages driving it.
Frequently asked
Can you run sentiment on earnings calls programmatically? Yes. A transcript source feeds an AI or lexicon scoring node that runs identically across every call, so the scores are comparable and you can rank by the change.
Why score prepared remarks and Q&A separately? Prepared remarks are written and lawyered; Q&A is answered live. Averaging them hides the gap, which is often more informative than either number alone.
Is one model best for this? It depends on the tradeoff you want between transparency and nuance. Lexicon scores are auditable; LLMs handle context and hedging better.
Build this pipeline in Cutonce
Chain data, filters, scoring, and AI nodes on a visual canvas. Free to start.