text-analytics-methods
Text analytics methodology. Referenced by topic-classifier and trend-detector agents when extracting topics and deriving trends from unstructured text. Used for 'topic classification', 'keyword analysis', 'text mining' requests. Note: NLP model training and large-scale data processing pipeline development are out of scope.
Best use case
text-analytics-methods is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Text analytics methodology. Referenced by topic-classifier and trend-detector agents when extracting topics and deriving trends from unstructured text. Used for 'topic classification', 'keyword analysis', 'text mining' requests. Note: NLP model training and large-scale data processing pipeline development are out of scope.
Teams using text-analytics-methods should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/text-analytics-methods/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How text-analytics-methods Compares
| Feature / Agent | text-analytics-methods | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Text analytics methodology. Referenced by topic-classifier and trend-detector agents when extracting topics and deriving trends from unstructured text. Used for 'topic classification', 'keyword analysis', 'text mining' requests. Note: NLP model training and large-scale data processing pipeline development are out of scope.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Text Analytics Methods
Enhances the text analysis capabilities of topic-classifier / trend-detector agents.
## Topic Classification Methodology
### Bottom-up Classification
```
1. Read all feedback (sample: 100+ entries)
2. Identify recurring keywords/phrases
3. Cluster similar keywords
4. Assign topic names to clusters
5. Finalize topic system (MECE verification)
6. Tag all data with topics
```
### Top-down Classification
```
Pre-defined categories:
├── Product/Service Quality
│ ├── Features
│ ├── Performance
│ ├── Design
│ └── Reliability
├── Customer Experience
│ ├── Usability
│ ├── Customer Support
│ ├── Purchase Process
│ └── Delivery
├── Price/Value
│ ├── Price Level
│ ├── Value for Money
│ └── Discounts/Promotions
└── Other
├── Competitor Comparison
└── Improvement Requests
```
## Keyword Analysis
### TF-IDF Concept Application
```
TF (Term Frequency) = Word frequency in a specific document
IDF (Inverse Document Frequency) = Rarity across all documents
TF-IDF = TF × IDF
High TF-IDF = Words important in a specific document but uncommon overall
→ Useful for identifying feedback anomalies
```
### Keyword Extraction Process
```
1. Remove stop words: articles, conjunctions, common verbs
2. Extract nouns/adjectives
3. Calculate frequency (top 30)
4. N-gram analysis (2-gram, 3-gram)
"slow delivery", "support not reachable"
5. Keyword cloud visualization
```
## Trend Analysis Techniques
### Time-series Topic Trends
| Topic | Q1 | Q2 | Q3 | Q4 | Trend |
|-------|-----|-----|-----|-----|-------|
| Delivery | 15% | 18% | 22% | 25% | ↑ Attention |
| Quality | 30% | 28% | 25% | 20% | ↓ Improving |
| Price | 20% | 22% | 20% | 21% | → Stable |
### Anomaly Detection Criteria
```
Anomaly signals:
- Topic ratio +50% vs previous period → urgent analysis
- Negative sentiment surge of +0.3 or more → investigate cause
- New keyword emergence (absent in previous quarter) → new issue
```
## Insight Derivation Framework
### Pyramid Principle (So What?)
```
Level 1 (Data): "Delivery complaints at 25%, up 7pp from previous quarter"
Level 2 (Analysis): "Delivery delays mainly caused by new logistics partner switch"
Level 3 (Insight): "Need to renegotiate logistics SLA or revert to previous provider"
Level 4 (Action): "Request SLA review from logistics team, improvement plan due in 2 weeks"
```
### Action Priority Matrix
| Insight | Frequency | Sentiment Intensity | Impact | Ease of Implementation | Priority |
|---------|-----------|--------------------| -------|----------------------|----------|
| Delivery improvement | High | -0.8 | High | Medium | ★★★★★ |
| UI improvement | Medium | -0.5 | Medium | High | ★★★★ |
| New feature A | Low | +0.3 | Low | Low | ★★ |
## Quality Checklist
| Item | Criteria |
|------|----------|
| Classification system | MECE (Mutually Exclusive, Collectively Exhaustive) |
| Sample size | 100+ entries or full dataset |
| Multi-tagging | Multiple topics per entry allowed |
| Trends | Minimum 3-period comparison |
| So What | Data → Analysis → Insight → Action |
| Visualization | Keyword cloud + trend chart |Related Skills
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