Cleaning a Noisy BNPL Listening Query

A single query should not serve both brand health and category intelligence. I would split them.

Category query (broad intent): ("buy now pay later" OR BNPL OR "installment payments" OR "pay in installments") AND (regulation OR fees OR credit OR checkout OR consumer OR merchant OR "late fee" OR affordability) NOT (job OR hiring OR career OR coupon OR "discount code" OR giveaway OR sweepstakes)

Brand query: ("Kite Pay" OR KitePay) NOT (job OR hiring OR career)

Why this shape:

• Quoted phrases prevent loose matching; the category terms capture how people actually talk about the product.
• The AND block requires a commercial or regulatory context, which removes generic jokes and retailer promotions.
• The NOT block removes known noise classes. It must be reviewed monthly, because exclusions can hide emerging risk.
• Apply English language and the four target countries at the query or dashboard level, not by eyeballing results.

For source control, separate news, forums and social. A regulatory story on a news site should not be mixed with X chatter without a label. The analyst should keep a small recall-guard query with fewer exclusions for crisis alerts, so precision work does not blind the team.

Volume alone is not evidence of quality. I would run a before-and-after precision check: pull a random sample of 150-200 mentions from each version, have the analyst code them as relevant or not, and calculate the relevance rate. If the filtered set moves from roughly 32% relevant to 70% or more, the drop is largely noise removal, not lost signal.

I would also test recall deliberately. Take a list of known regulatory terms, competitor names and crisis phrases and confirm they still return results. Keep a low-exclusion guard query and compare its top clusters weekly. If the guard query surfaces a risk theme absent from the clean query, the exclusions are too aggressive.

Monitor four things:

• Volume anomalies: a sudden fall in category mentions can indicate a query break, not a quiet market.
• Source mix: if news disappears while social remains, source filters may be wrong.
• Sentiment and topic drift: a new fee or regulatory theme should appear in clusters.
• Excluded-term sample: review a sample of excluded posts monthly for false negatives.

The 5,200 figure is acceptable only if precision improved and the recall check passes. Otherwise the team has optimized for a tidy dashboard and weakened risk detection.

I would not report 41% as Kite Pay's brand sentiment. The query is a category query, and the setup says the negativity is driven by regulatory news. That is market sentiment about buy-now-pay-later, not necessarily sentiment toward the brand. Presenting it as brand sentiment would mislead PR and product.

Next I would separate and diagnose:

• Compare brand-query sentiment with category-query sentiment.
• Break sentiment down by source type: news, forums, X and review sites often have different baselines.
• Cluster the negative mentions into themes such as fees, regulation, debt, customer service or competitor comparisons.
• Manually validate a sample of 100 negative mentions to check whether the sentiment model is misreading sarcasm or neutral regulatory language.

Then I would report a short set of labeled themes with representative examples, not one aggregate score. If regulation dominates, the briefing should say that, name the regulators or markets involved, and distinguish reputational risk from direct brand criticism. The diagnostic step is theme and source decomposition, followed by human validation. That gives PR an actionable view and avoids treating a category-wide mood as a brand crisis.