Project: publish a reviewed support-theme monitor

Define the report

Support leads need the most common actionable issues by product area, with a short list of new or rising themes. Decide the eligible ticket population and what one counted case means. Exclude duplicate replies rather than counting the same problem twice. Preserve product, language, channel and capture time as analysis dimensions. Do not put raw sensitive text into an unrestricted dashboard; show only reviewed excerpts behind access control.

Build candidate themes

Start with existing support tags and a sparse baseline, then compare a topic model or embedding clustering on the same frozen corpus. Review representative tickets and cluster edges. Name only themes that reviewers can define consistently; keep mixed and unresolved clusters visible. Corpus and model choice explains why a readable word list is not enough. Freeze an anchor set of difficult tickets for the next model update.

Guard the trend line

When retraining, assign old and new models to the anchor set. Review splits, merges and unresolved mappings before joining time series. If label policy, channel mix or language coverage changes, start a new series or annotate the break. Alignment and drift review supplies that version contract. Compare raw volume and rate per eligible tickets so a surge in overall support traffic does not masquerade as a new issue.

Publish with review capacity

The weekly package contains reviewed theme names, support counts, representative examples for authorized leads and a confidence note for each trend. A human approves the new-theme list; the model proposes candidates. Monitor reviewer disagreement, theme coverage, unresolved volume and time spent per review. If a rare high-impact topic is swallowed by a broad cluster, maintain a targeted rule or classifier alongside discovery.

Implementation

def theme_share(theme_counts, eligible_cases):
    if eligible_cases <= 0:
        raise ValueError("eligible case count must be positive")
    if any(count < 0 for count in theme_counts.values()):
        raise ValueError("theme counts cannot be negative")
    return {theme: count / eligible_cases for theme, count in theme_counts.items()}

weekly = theme_share({"callback-retry": 47, "refund-delay": 23}, 940)
assert weekly["callback-retry"] == 0.05

Performance and operating cost

Calculating shares is O(t) time and O(t) output space for t reviewed themes. Training and periodic reassignment dominate compute, while human naming and alignment dominate editorial cost. Track the fraction of cases assigned to no useful theme; a small, stable dashboard can look healthy while coverage collapses. Use the same denominator policy for every reported week.

Common Mistakes

• Counting every reply as an independent customer problem.
• Joining retrained topic IDs without reviewed alignment.
• Reporting raw volume as a rising share of support demand.
• Dropping unresolved clusters so theme coverage looks stronger.

Read next

• Topic discovery: corpus boundaries, model choice and human labels
• Topic drift: align changing clusters before reporting trends
• Text validation: split conversations, duplicates and time together
• Project: turn mixed product feedback into reviewed aspect signals
• Project: enforce a privacy-safe support-text pipeline