Sentiment Scoring
Enhances the sentiment-analyst agent's sentiment classification and scoring capabilities.
Sentiment Classification System
3-Level Basic Classification
| Classification |
Score Range |
Signal Words |
| Positive |
+0.5 to +1.0 |
good, satisfied, recommend, convenient, best |
| Neutral |
-0.5 to +0.5 |
average, okay, so-so |
| Negative |
-1.0 to -0.5 |
dissatisfied, disappointed, worst, inconvenient, slow |
5-Level Detailed Classification
| Classification |
Score |
Expression Examples |
| Very Positive |
+0.8 to +1.0 |
"Absolutely the best!", "Highly recommend" |
| Positive |
+0.3 to +0.7 |
"Pretty good", "Satisfied" |
| Neutral |
-0.2 to +0.2 |
"It's average", "Not bad" |
| Negative |
-0.7 to -0.3 |
"Somewhat inconvenient", "Below expectations" |
| Very Negative |
-1.0 to -0.8 |
"Never using this again", "Worst ever" |
Sentiment Analysis Methodology
Rule-based Analysis
1. Tokenize: Sentence → word/phrase segmentation
2. Sentiment dictionary matching:
- Positive words (+1): satisfied, good, convenient, recommend...
- Negative words (-1): dissatisfied, disappointed, inconvenient, slow...
- Intensifiers (×1.5): very, really, extremely, totally...
- Negators (×-1): not, no, cannot, never...
3. Sentence score = Σ(word scores) / word count
4. Document score = Σ(sentence scores) / sentence count
Context Correction Rules
| Pattern |
Handling |
Example |
| Positive + "but" + Negative |
Weight latter part |
"Good but expensive" → -0.3 |
| Negative + "but" + Positive |
Weight latter part |
"Expensive but good" → +0.3 |
| Sarcasm |
Invert |
"Really impressive (sarcastic)" → -0.5 |
| Comparison |
Relative evaluation |
"B is better than A" → B positive |
| Conditional |
Weaken |
"It would be nice if..." → -0.2 |
NPS (Net Promoter Score) Analysis
NPS Calculation
NPS Question: "Would you recommend this product?" (0-10 scale)
Promoter: 9-10
Passive: 7-8
Detractor: 0-6
NPS = Promoter% - Detractor%
Range: -100 to +100
NPS Benchmark
| NPS Range |
Rating |
Interpretation |
| 70+ |
World-class |
Apple, Tesla tier |
| 50-69 |
Excellent |
Industry leader |
| 30-49 |
Good |
Above average |
| 0-29 |
Needs improvement |
Average level |
| Below 0 |
At risk |
Immediate action needed |
Sentiment Trend Analysis
Time-series Sentiment Tracking
Monthly sentiment score changes:
Jan: +0.45 (positive-dominant)
Feb: +0.38 (slight decline)
Mar: -0.12 (negative shift) ← event investigation needed
Apr: +0.22 (recovering)
Anomaly detection threshold:
±0.3 or greater change from previous month → root cause analysis required
Quality Checklist
| Item |
Criteria |
| Classification system |
Consistent 3-level or 5-level application |
| Context correction |
Inversion/intensification/conditional handling |
| Confidence interval |
Ambiguous cases treated as neutral |
| NPS calculation |
Standard formula applied |
| Trend |
Time-series tracking + anomaly detection |
1---2name: sentiment-scoring3description: Sentiment analysis scoring framework. Referenced by the sentiment-analyst agent for systematic sentiment classification and scoring of text data. Used for 'sentiment analysis', 'emotion score', 'NPS analysis' requests. Note: ML model training and NLP pipeline development are out of scope.4---56# Sentiment Scoring78Enhances the sentiment-analyst agent's sentiment classification and scoring capabilities.910## Sentiment Classification System1112### 3-Level Basic Classification1314| Classification | Score Range | Signal Words |15|---------------|------------|--------------|16| Positive | +0.5 to +1.0 | good, satisfied, recommend, convenient, best |17| Neutral | -0.5 to +0.5 | average, okay, so-so |18| Negative | -1.0 to -0.5 | dissatisfied, disappointed, worst, inconvenient, slow |1920### 5-Level Detailed Classification2122| Classification | Score | Expression Examples |23|---------------|-------|-------------------|24| Very Positive | +0.8 to +1.0 | "Absolutely the best!", "Highly recommend" |25| Positive | +0.3 to +0.7 | "Pretty good", "Satisfied" |26| Neutral | -0.2 to +0.2 | "It's average", "Not bad" |27| Negative | -0.7 to -0.3 | "Somewhat inconvenient", "Below expectations" |28| Very Negative | -1.0 to -0.8 | "Never using this again", "Worst ever" |2930## Sentiment Analysis Methodology3132### Rule-based Analysis3334```351. Tokenize: Sentence → word/phrase segmentation362. Sentiment dictionary matching:37 - Positive words (+1): satisfied, good, convenient, recommend...38 - Negative words (-1): dissatisfied, disappointed, inconvenient, slow...39 - Intensifiers (×1.5): very, really, extremely, totally...40 - Negators (×-1): not, no, cannot, never...413. Sentence score = Σ(word scores) / word count424. Document score = Σ(sentence scores) / sentence count43```4445### Context Correction Rules4647| Pattern | Handling | Example |48|---------|----------|---------|49| Positive + "but" + Negative | Weight latter part | "Good but expensive" → -0.3 |50| Negative + "but" + Positive | Weight latter part | "Expensive but good" → +0.3 |51| Sarcasm | Invert | "Really impressive (sarcastic)" → -0.5 |52| Comparison | Relative evaluation | "B is better than A" → B positive |53| Conditional | Weaken | "It would be nice if..." → -0.2 |5455## NPS (Net Promoter Score) Analysis5657### NPS Calculation5859```60NPS Question: "Would you recommend this product?" (0-10 scale)6162Promoter: 9-1063Passive: 7-864Detractor: 0-66566NPS = Promoter% - Detractor%67Range: -100 to +10068```6970### NPS Benchmark7172| NPS Range | Rating | Interpretation |73|-----------|--------|----------------|74| 70+ | World-class | Apple, Tesla tier |75| 50-69 | Excellent | Industry leader |76| 30-49 | Good | Above average |77| 0-29 | Needs improvement | Average level |78| Below 0 | At risk | Immediate action needed |7980## Sentiment Trend Analysis8182### Time-series Sentiment Tracking8384```85Monthly sentiment score changes:86 Jan: +0.45 (positive-dominant)87 Feb: +0.38 (slight decline)88 Mar: -0.12 (negative shift) ← event investigation needed89 Apr: +0.22 (recovering)9091Anomaly detection threshold:92 ±0.3 or greater change from previous month → root cause analysis required93```9495## Quality Checklist9697| Item | Criteria |98|------|----------|99| Classification system | Consistent 3-level or 5-level application |100| Context correction | Inversion/intensification/conditional handling |101| Confidence interval | Ambiguous cases treated as neutral |102| NPS calculation | Standard formula applied |103| Trend | Time-series tracking + anomaly detection |
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