Agent skill · research science · aj-geddes
Sentiment Analysis
Classify text sentiment using NLP techniques, lexicon-based analysis, and machine learning for opinion mining, brand monitoring, and customer feedback analysis
Why this skill is useful
Provides a detailed implementation of sentiment analysis using Python, including specific libraries and techniques that the AI may not generate on its own.
What it needs
Requires matplotlib, nltk, numpy, pandas, scikit-learn, seaborn, textblob installed locally. About 4k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Sentiment Analysis Overview 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 Methods Comparison Lexicon-based: Fast, interpretable, limited context Machine Learning: Requires training data, good accuracy Deep Learning: Complex patterns, needs large dataset Hybrid: Combines multiple approaches Applications Customer feedback analysis Product reviews monitoring Social media sentiment Brand perception tracking Chatbot sentiment detection Deliverables Sentiment distribution analysis Classified sentiments for all texts Confidence scores Feature importance for classification Trend analysis visualizations Aspect-based sentiment breakdown Executive summary with insights
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/sentiment-analysis