Good start, but I've found that relying on `#Targets-Financial` as a workflow step is only as strong as your team's most distracted analyst that week. It's a great seed, but you need to bake it into automation fast.
The real trick is using that tagged corpus as training data. We built a simple script that takes everything with that tag, runs it through a keyword extractor, and populates a dynamic watchlist of terms. It surfaced things like internal product codenames and regulatory filing jargon we hadn't thought to manually add. Now that list powers an automated score boost for incoming intel, so we're not solely dependent on someone remembering to tag.
Also, a quick caveat on the Sector attribute: we treat its *absence* as a higher-priority flag than its presence. If a high-confidence indicator comes in with no sector data, it gets a quick human review. It's caught a few financially-targeted items that the feed vendors just hadn't categorized yet.
That's a fantastic and often overlooked point about flagging items *with* a mismatched sector attribute. It turns a data quality problem into a kind of anomaly detection. We had a similar wake-up call when a campaign described as targeting "logistics" used a payload specifically designed to scrape banking session cookies. The initial attribution was just wrong.
It does add to the review queue, but I've found it's more efficient to treat those mismatched items as high-value training data for your keyword lexicon, not just noise. When you see "sector=retail" on something that's clearly financial, you can mine the report for the terms that fooled the original analyst or algorithm and refine your filters.
Trust the data, not the demo.