Catch escalation early
Complaints, disputes, and negative spikes surface as scored incidents the moment they cluster, so reputation risk is flagged while it is still a few messages, not a thread on fire.
Modules
Sentiment Monitor
SENTIMENT MONITOR
See community risk forming, not after it breaks
Aurora reads every message in real time, classifies mood and risk, and groups related complaints into tracked incidents backed by evidence. Your team works the highest-priority issues first instead of scrolling channels hoping to catch trouble early.
End manual channel-watching
Alerts fire on data and tunable thresholds, not gut feel. The team stops staring at channels and spends its hours resolving issues instead.
A classifier that learns your community
Operators correct a misclassification in one click, and a second-opinion AI review folds that context back in, so accuracy climbs on your community's slang, topics, and tone.
HOW IT WORKS
From raw chatter to a prioritized queue
Classify in real time
Every message is scored for sentiment and risk as it arrives, with matched keywords and context kept as evidence. Detection runs in-house, no external API required.
Group into incidents
Related signals across channels collapse into one tracked incident with impact radius, severity, and an evidence trail, so the team triages once instead of ten times.
Alert, report, resolve
Threshold alerts and a scheduled AI daily report land in Discord with clear next actions. Operators move each incident through Open, Reviewing, and Resolved.
CAPABILITIES
Built for teams running real communities
Incident lifecycle
Open, Reviewing, Resolved, and Muted statuses with cross-channel correlation, impact radius, and an evidence trail behind every alert.
Scheduled AI daily report
A prioritized community readout delivered to Discord on your schedule and timezone, in English or Chinese, with channel-level evidence and recommended actions.
Tunable sensitivity
Strict, Balanced, and Quiet presets or custom thresholds for negative-discussion share and risk-message counts, with dynamic baselines for high-traffic communities.
Evidence-based investigation
Drill into any incident to see representative messages, matched keywords, risk scores, and the context rules that suppressed false positives.
Correctable classifier
Flag a misclassification to trigger a second-opinion AI review, then apply the suggested context rule in one click to sharpen future detection.
Scope and delivery controls
Filter analysis by channel and role, define a community profile that separates owned products from third-party chatter, and fan reports out to Feishu, Slack, or generic webhooks.
Start with Aurora
Turn community noise into early warning
Give your operators a monitor that detects, groups, and reports risk on its own, so the team responds early, acts on evidence, and scales across every channel.