1. What sentiment_score really measures
Sentiment score = emotional valence
Positive ↔ Neutral ↔ Negative
It answers:
“How does the person feel about this topic right now?”
Not:
- How important it is
- How much effort they’re putting in
- Whether they’ll act
You can have:
- High engagement + negative sentiment
- Low engagement + positive sentiment
That’s why this field matters.
2. Recommended scale (keep it simple)
Option A — Discrete scale (best for explainability)
| Score | Label | Meaning |
|---|---|---|
| -2 | Strongly Negative | Frustration, anger, resentment |
| -1 | Negative | Dislike, concern, reluctance |
| 0 | Neutral / Mixed | Informational, balanced |
| +1 | Positive | Enjoyment, optimism |
| +2 | Strongly Positive | Excitement, pride, love |
This is human-legible and stable over time.
Option B — Normalized (-1.0 → +1.0)
Use only if you’re piping NLP outputs directly.
| Range | Interpretation |
|---|---|
| -1.0 to -0.6 | Strong negative |
| -0.59 to -0.2 | Mild negative |
| -0.19 to +0.19 | Neutral |
| +0.2 to +0.59 | Mild positive |
| +0.6 to +1.0 | Strong positive |
You can always bucket it later.
3. Examples (this is the important part)
A. Same topic, different sentiment
Topic: Work / Career
| Utterance | sentiment_score | Why |
|---|---|---|
| “Work is fine.” | 0 | Flat, informational |
| “I like my job.” | +1 | Mild positive |
| “I love what I’m building.” | +2 | Strong positive |
| “I’m stuck at work.” | -1 | Frustration |
| “I hate my job.” | -2 | Strong negative |
Engagement could be high in all of these.
B. Same sentiment, different engagement
Topic: Health & Gyms
| Utterance | Sentiment | Engagement |
|---|---|---|
| “Gyms are great.” | +1 | 1 |
| “I train 5x a week and track PRs.” | +1 | 5 |
Sentiment = feeling
Engagement = energy
C. Business context examples
Topic: Project Management
| Utterance | sentiment_score |
|---|---|
| “We’re on track.” | +1 |
| “This is harder than expected.” | -1 |
| “This project is a mess.” | -2 |
| “Challenging, but solvable.” | 0 |
Topic: Negotiations & Proposals
| Utterance | sentiment |
|---|---|
| “Let’s explore options.” | 0 |
| “I’m excited to move forward.” | +2 |
| “This pricing doesn’t work.” | -1 |
| “This is unacceptable.” | -2 |
D. Personal & emotional topics
Topic: Emotions / Love
| Utterance | sentiment_score |
|---|---|
| “I’m okay.” | 0 |
| “I feel close to her.” | +2 |
| “I’m confused.” | -1 |
| “I’m heartbroken.” | -2 |
This is where sentiment is most valuable.
4. Common modeling mistakes (avoid these)
❌ Mistake 1: Using sentiment as importance
“This is a big issue” ≠ negative sentiment
❌ Mistake 2: Using sentiment as an outcome
“We closed the deal” ≠ positive sentiment
❌ Mistake 3: Averaging sentiment blindly
A -2 followed by +2 is not “neutral” — it’s volatility.
5. How sentiment works with your existing fields
| Field | Question it answers |
|---|---|
| Topic | What are we talking about |
| Intent | Why now |
| Engagement score | How invested |
| Sentiment score | How it feels |
| Outcome | What happened |
This separation is why your model is actually strong.
6. Design recommendation (this is subtle but important)
Store:
sentiment_scoresentiment_confidence(low / medium / high)
Why?
Some statements are emotionally clear. Some are ambiguous.
Example:
- “Interesting.” → sentiment unclear → low confidence
Summary
- Sentiment = emotional direction
- Use -2 to +2
- Keep it independent of engagement & outcome
- Anchor it to language, not interpretation
//todo
- Combine sentiment × engagement into a “heat map.”
- Model sentiment drift over time
- Or detect hidden risk (high engagement + negative sentiment)
That’s where this turns into a real signal, not just labels.
1. What engagement_score really measures
Engagement score = how much energy + commitment is present in the interaction.
Not sentiment.
Not importance.
Not outcome.
It answers:
“How invested is the person in this conversation right now?”
Think attention × effort × continuity.
2. Signal strength (the inputs)
signal_strength is evidence, not judgment.
Common observable signals
| Signal | Examples |
|---|---|
| Time | Long messages, long calls, repeat back-and-forth |
| Depth | Personal detail, specificity, vulnerability |
| Effort | Preparation, documents shared, follow-ups |
| Agency | Decisions made, commitments stated |
| Continuity | Refers to past conversations, future plans |
| Risk | Disagreement, asking for advice, asking for help |
Each of these increases signal strength.
3. Example scales (practical, not academic)
Option A — Simple 1–5 scale (recommended)
This works shockingly well.
| Score | Label | Observable signals |
|---|---|---|
| 1 | Passive | Polite replies, small talk, low effort |
| 2 | Light | Casual interest, short responses |
| 3 | Engaged | Asking questions, staying on topic |
| 4 | Invested | Sharing context, opinions, time |
| 5 | Committed | Decisions, plans, emotional or professional stake |
Option B — Expanded 0–100 (only if you need math)
Use if you plan analytics.
| Range | Meaning |
|---|---|
| 0–20 | Noise |
| 21–40 | Awareness |
| 41–60 | Interest |
| 61–80 | Engagement |
| 81–100 | Commitment |
Internally, you can still bucket it to 1–5.
4. Mapping signal strength → engagement_score
You can model it explicitly.
Example signals → score
| Signals present | engagement_score |
|---|---|
| Topic hopping, generic replies | 1 |
| Jokes, light banter | 2 |
| Asking follow-ups | 3 |
| Sharing constraints, goals | 4 |
| Making decisions / asking next steps | 5 |
5. Real examples from your domain
Example 1: Salsa Dancing
- “Yeah I dance sometimes” → 1
- “I love salsa, where do you dance?” → 3
- “I’m training for a festival, practicing twice a week” → 5
Topic stayed the same.
Engagement changed drastically.
Example 2: Business — Marketing Campaigns
- “Send me the deck” → 2
- “What would success look like?” → 3
- “Let’s launch next month, budget is X” → 5
Example 3: Personal — Aspirational
- “I’ve been thinking about my career” → 3
- “I’m unhappy and need to change direction” → 4
- “I’m committing to a 90-day pivot” → 5
6. Where this fits in your model
Think in layers:
- Topic → What
- Intent → Why now
- Learning touchpoint → Where in the arc
- Engagement score → How much energy
- Signal strength → Why you believe that score
This keeps the model explainable, not magical.
7. Design advice (important)
- Don’t over-optimize this.
- Human-readable beats perfect.
- Engagement is situational, not identity.
A person can be:
Engagement = 5in SalsaEngagement = 1in Investing
That’s not inconsistency — that’s truth.
Summary
- Signal strength = observable evidence
- Engagement score = normalized judgment of investment
- Use 1–5 unless you really need more
- Anchor every score to something you could point to
//todo
- Define decay over time
- Weight engagement by intent
- design a query like: “Which topics generate high engagement but low outcomes?”
lethal design in a good way.
Engagement is situational, but Energy and Effectiveness is Person Centric
ENERGY ASSESSMENT (Circle 0-10)
- Pre-interaction mood: Dread [0 1 2 3 4 5 6 7 8 9 10] Anticipation
- Post-interaction feeling: Drained [0 1 2 3 4 5 6 7 8 9 10] Energized
- Conversation flow: Strained [0 1 2 3 4 5 6 7 8 9 10] Natural
- Emotional safety: Unsafe [0 1 2 3 4 5 6 7 8 9 10] Safe
EFFECTIVENESS ASSESSMENT (Circle 0-10)
- Follow-through: Never [0 1 2 3 4 5 6 7 8 9 10] Always
- Value created: None [0 1 2 3 4 5 6 7 8 9 10] Transformative
- Proactivity: Passive [0 1 2 3 4 5 6 7 8 9 10] Initiator
- Efficiency: Time-waster [0 1 2 3 4 5 6 7 8 9 10] Multiplier
Usage: Scoring and creating a recommendation engine to determine a contact frequency
The scores mean:
- E: 73 = Engagement Score (0-100) – Measures encounter frequency and patterns. Calculated from total encounters, recent activity (last 90 days), channel diversity, and consistency.
- R: 32 = Relationship Score (0-100) – Measures relationship quality based on NAP intimacy level, NAP impact level, and relationship category (Power/Master Mind gets bonus points).
- Rc: 95 = Recency Score (0-100) – Measures recent interaction patterns. Based on activity in the last 30 days and days since last contact.
These three scores are combined with weighted averages to determine the recommended contact frequency for that person. Higher scores generally indicate that more frequent contact is warranted.
