Analyze sentiment in text using ML models. Use when: analyzing customer reviews; processing NPS feedback; monitoring brand mentions; evaluating campaign responses; categorizing support tickets
The English summary is being prepared. Showing the original description.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Analyze sentiment in customer feedback using transformer models - understand what your customers really feel at scale.
| Claude Does | You Decide |
|---|---|
| Structures analysis frameworks | Metric definitions |
| Identifies patterns in data | Business interpretation |
| Creates visualization templates | Dashboard design |
| Suggests optimization areas | Action priorities |
| Calculates statistical measures | Decision thresholds |
pip install transformers torch pandas click
# Or for lighter CPU-only version:
pip install textblob vaderSentiment pandas click
python scripts/main.py analyze "This product exceeded my expectations!"
python scripts/main.py analyze "The service was terrible and slow."
python scripts/main.py batch reviews.csv --column text
python scripts/main.py batch feedback.csv --column comment --output results.csv
python scripts/main.py report reviews.csv --column text --output sentiment-report.html
# Process CSV of reviews
python scripts/main.py batch amazon-reviews.csv --column review_text
# Output: amazon-reviews_sentiment.csv
# review_text | sentiment | score | label
# "Absolutely love this!" | positive | 0.95 | Very Positive
# "It's okay, nothing special" | neutral | 0.52 | Neutral
# "Worst purchase ever" | negative | 0.12 | Very Negative
# Analyze NPS survey responses
python scripts/main.py report nps-responses.csv --column feedback
# Output: sentiment-report.html
# Summary:
# - Positive: 62% (mainly: product quality, support)
# - Neutral: 23% (mainly: pricing concerns)
# - Negative: 15% (mainly: shipping delays)
| Score Range | Label | Interpretation |
|---|---|---|
| 0.8 - 1.0 | Very Positive | Enthusiastic, recommend |
| 0.6 - 0.8 | Positive | Satisfied, happy |
| 0.4 - 0.6 | Neutral | Mixed or indifferent |
| 0.2 - 0.4 | Negative | Disappointed, frustrated |
| 0.0 - 0.2 | Very Negative | Angry, will churn |
category: analytics
subcategory: nlp
dependencies: [transformers, torch, pandas]
difficulty: intermediate
time_saved: 6+ hours/week
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
The English summary is being prepared. Showing the original description.
Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
The English summary is being prepared. Showing the original description.
Assess customer account health using product usage, support sentiment, payment status, and relationship signals
The English summary is being prepared. Showing the original description.
Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
The English summary is being prepared. Showing the original description.
Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.
The English summary is being prepared. Showing the original description.
Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps.
The English summary is being prepared. Showing the original description.