Sentiment analysis determines emotional tone and opinions in text, enabling understanding of customer satisfaction, brand perception, and feedback analysis.
Fetches sentiment-analysis from aj-geddes/useful-ai-prompts and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
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4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/sentiment-analysis
Restart Cursor to activate sentiment-analysis. Access via /sentiment-analysis in your agent's command palette.
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Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Sentiment analysis determines emotional tone and opinions in text, enabling understanding of customer satisfaction, brand perception, and feedback analysis.
Approaches
Lexicon-based: Using sentiment dictionaries
Machine Learning: Training classifiers on labeled data
Deep Learning: Neural networks for complex patterns
Aspect-based: Sentiment about specific features
Multilingual: Non-English text analysis
Sentiment Types
Positive: Favorable, satisfied
Negative: Unfavorable, dissatisfied
Neutral: Factual, no clear sentiment
Mixed: Combination of sentiments
Implementation with Python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import re
from collections import Counter
# Sample review datareviews_data =["This product is amazing! I love it so much.","Terrible quality, very disappointed.","It's okay, nothing special.","Best purchase ever! Highly recommend.","Worst product I've ever bought.","Pretty good, satisfied with the purchase.","Excellent service and fast delivery.","Poor quality and bad customer support.","Not bad, does what it's supposed to.","Absolutely fantastic! Five stars!","Mediocre product, expected better.","Love everything about this!","Complete waste of money.","Good value for the price.","Very satisfied, will buy again!","Horrible experience from start to finish.","It works as described.","Outstanding quality and design!","Disappointed with the results.","Perfect! Exactly what I wanted.",]sentiments =['Positive','Negative','Neutral','Positive','Negative','Positive','Positive','Negative','Neutral','Positive','Negative','Positive','Negative','Positive','Positive','Negative','Neutral','Positive','Negative','Positive']df = pd.DataFrame({'review': reviews_data,'sentiment': sentiments})print("Sample Reviews:")print(df.head(10))# 1. Lexicon-based Sentiment Analysisfrom nltk.sentiment import SentimentIntensityAnalyzer
try:import nltk
nltk.download('vader_lexicon', quiet=True) sia = SentimentIntensityAnalyzer() df['vader_scores']= df['review'].apply(lambda x: sia.polarity_scores(x)) df['vader_compound']= df['vader_scores'].apply(lambda x: x['compound']) df['vader_sentiment']= df['vader_compound'].apply(lambda x:'Positive'if x >0.05else('Negative'if x <-0.05else'Neutral'))print("\n1. VADER Sentiment Scores:")print(df[['review','vader_compound','vader_sentiment']].head())except:print("NLTK not available, skipping VADER analysis")# 2. Textblob Sentiment (alternative)try:from textblob import TextBlob
df['textblob_polarity']= df['review'].apply(lambda x: TextBlob(x).sentiment.polarity) df['textblob_sentiment']= df['textblob_polarity'].apply(lambda x:'Positive'if x >0.1else('Negative'if x <-0.1else'Neutral'))print("\n2. TextBlob Sentiment Scores:")print(df[['review','textblob_polarity','textblob_sentiment']].head())except:print("TextBlob not available")# 3. Feature Extraction for MLvectorizer = TfidfVectorizer(max_features=100, stop_words='english')X = vectorizer.fit_transform(df['review'])y = df['sentiment']print(f"\n3. Feature Matrix Shape: {X.shape}")print(f"Features extracted: {len(vectorizer.get_feature_names_out())}")# 4. Machine Learning ModelX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)# Naive Bayes classifiernb_model = MultinomialNB()nb_model.fit(X_train, y_train)y_pred = nb_model.predict(X_test)print("\n4. Machine Learning Results:")print(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}")print("\nClassification Report:")print(classification_report(y_test, y_pred))# 5. Sentiment Distributionfig, axes = plt.subplots(2,2, figsize=(14,8))# Distribution of sentimentssentiment_counts = df['sentiment'
✓
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
