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Score the bundled headlines, aggregate them into a daily signal, and write a grounded summary. Usage /sentiment [YYYY-MM-DD]
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| name | sentiment |
| description | Score the bundled headlines, aggregate them into a daily signal, and write a grounded summary. Usage /sentiment [YYYY-MM-DD] |
Run the score -> aggregate -> summarize pipeline and save a report. With no date, process every bundled day; with a date, process just that one.
python -m tools.aggregate > reports/_signal.json
# or for one day:
python -m tools.aggregate --date 2024-02-13 > reports/_signal.json
reports/_signal.json: each date has a mean, a label, a count, and the
item_ids that fed it; headlines holds each scored item.python -m tools.lexicon "<headline>" to see which words and modifiers moved it.mean and label verbatim and cites the headlines that drove it by id. Never state
a polarity or mean that is not in reports/_signal.json.reports/<date>.md (or reports/signal.md for all dates):
Try these to start:
/sentiment — all three days; expect a bullish, a bearish and a neutral day./sentiment 2024-02-13 — the bearish day; see which headline weighed most./sentiment 2024-02-14 — a near-zero day where one negated headline ("does not beat")
cancels a positive one; a good test of whether the summary respects the tool's signs.