EMOTIONAL HEALTH
Sentiment Tracking & Analysis
VIRCON continuously monitors community sentiment using NLP and emoji analysis to detect emotional trends, staff burnout, and community health concerns. Sentiment tracking enables proactive intervention and data-driven policy decisions.
Sentiment Commands
Commands for analyzing user and community sentiment trends over time.
- ,sentiment @user [days] - View sentiment trend for specific user
- ,sentiment overall [days] - Community-wide sentiment analysis
- ,sentiment channel #channel [days] - Sentiment in specific channel
- ,sentiment staff [days] - Staff emotional health metrics
- ,sentiment alert <threshold> - Set alert for negative sentiment spikes
Tracked Sentiment Metrics
Key emotional indicators measured through NLP and behavioral analysis.
Message Tone
Positive, neutral, negative classification of chat messages
Method: NLP sentiment analysis on message content
Scale: -1.0 (very negative) to +1.0 (very positive)
Emoji Usage
Ratio of positive vs negative emoji reactions
Method: Sentiment mapping of emoji to emotional valence
Scale: Percentage of positive emoji per 100 messages
Conversation Length
Average message count before user disengages
Method: Track conversation thread participation duration
Scale: Messages per conversation (higher = more engagement)
Staff Interaction Quality
User sentiment before and after staff interaction
Method: Compare sentiment 5 messages before/after staff reply
Scale: Delta from -2.0 to +2.0 (sentiment shift)
Analysis Workflow
Continuous sentiment monitoring pipeline from message analysis to trend visualization.
- 1. System continuously monitors all messages for sentiment indicators.
- 2. NLP engine analyzes text for emotional tone, keywords, emoji usage.
- 3. Sentiment score assigned to each message on -1.0 to +1.0 scale.
- 4. Scores aggregated into 30-minute buckets for trend analysis.
- 5. Daily sentiment averages calculated per user, channel, server-wide.
- 6. Anomaly detection alerts when sentiment drops >0.3 points in 24h.
- 7. Weekly reports generated showing sentiment trends and concerning patterns.
- 8. Dashboard visualizes sentiment heatmap by time of day and day of week.
Sentiment Database Schema
Database structure for storing sentiment scores, confidence levels, and keyword analysis.
| Field | Description |
|---|---|
| sentiment_id | Unique sentiment record identifier. |
| user_discord_id | User whose message was analyzed. |
| channel_id | Channel where message was posted. |
| message_id | Message ID for audit trail. |
| message_timestamp | When message was sent. |
| sentiment_score | -1.0 to +1.0 emotional tone rating. |
| sentiment_label | positive, neutral, negative classification. |
| confidence | 0-100 confidence in sentiment classification. |
| emoji_count | Number of emoji in message. |
| emoji_sentiment | Aggregated emoji emotional valence. |
| keywords | Array of detected sentiment keywords. |
Alerting Features
Automated notifications for sentiment drops, spikes, and staff burnout indicators.
- Threshold alerts: notify when user or channel sentiment drops below threshold.
- Spike detection: flag sudden sentiment drops (>0.3 change in 24h).
- Staff wellness: alert admin if staff sentiment < -0.2 for 3+ days.
- Community health: warn if server-wide sentiment trending negative for 5+ days.
- Pattern recognition: detect sentiment patterns correlating with specific events.
Real-World Use Cases
Practical examples of sentiment analysis enabling proactive support and policy adjustments.
Case 1
User sentiment suddenly drops from +0.6 to -0.4 over 48 hours
Action: System triggers alert to staff channel
Outcome: Staff reaches out proactively, discovers user having personal crisis, provides support
Case 2
Overall community sentiment trending negative for 5 days straight
Action: Admin runs ,sentiment overall 7 to analyze trend
Outcome: Identifies recent policy change causing frustration, adjusts policy based on data
Case 3
Staff member shows declining sentiment after moderation actions
Action: Head admin runs ,sentiment staff 30
Outcome: Identifies moderator burnout, assigns time off and redistributes workload
Privacy Considerations
Data retention policies and access controls protecting user privacy.
- Sentiment data does NOT store message content, only sentiment scores.
- Individual message sentiment NOT accessible to non-admin staff.
- Aggregated data (daily/weekly averages) available to staff for trends.
- User-specific sentiment only viewable by admins for intervention purposes.
- Sentiment data retention: 90 days, then aggregated to weekly averages only.
Best Practices
Guidelines for ethical and effective use of sentiment data for community wellness.
- Use sentiment trends for early intervention, not punishment.
- Combine sentiment data with other context (incidents, staff interactions).
- Review staff sentiment weekly to catch burnout before resignation.
- Share positive sentiment trends with community to celebrate successes.
- Investigate sudden negative spikes promptly - often indicates unmet needs.
- Compare sentiment before/after policy changes to measure impact.
Limitations & Accuracy
Known constraints and accuracy considerations for NLP-based sentiment analysis.
- Sarcasm and irony can confuse sentiment analysis (lower confidence scores).
- Short messages (<5 words) have lower accuracy than longer messages.
- Cultural differences in emoji usage may affect sentiment classification.
- Sentiment scores are indicators, not definitive measures of user well-being.
- Requires 7+ days of data for meaningful trends (avoid snap judgments).