Sentiment Analysis
Sentiment Analysis determines emotional tone in text through lexicon-based, machine learning, and deep learning approaches. It categorizes content as positive, negative, neutral, or mixed—useful for understanding customer satisfaction, monitoring brand perception, and extracting insights from feedback.
Sentiment Analysis classifies text emotion to reveal customer opinions and satisfaction levels.
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Install
aj-geddes/useful-ai-prompts/sentiment-analysis · repository language: Shell
git clone https://github.com/aj-geddes/useful-ai-prompts
cp -r useful-ai-prompts/skills/sentiment-analysis ~/.claude/skills/sentiment-analysisnpx skillfed install aj-geddes/useful-ai-prompts/sentiment-analysisFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What does Sentiment Analysis do?
Sentiment Analysis determines emotional tone in text through lexicon-based, machine learning, and deep learning approaches. It categorizes content as positive, negative, neutral, or mixed—useful for understanding customer satisfaction, monitoring brand perception, and extracting insights from feedback.
Can Sentiment Analysis classify text sentiment to understand customer opinions?
Yes. Sentiment Analysis classifies text sentiment to understand customer opinions and satisfaction. Its primary use (35% of intent weight) is categorizing customer-generated content—reviews, comments, messages—into positive, negative, neutral, or mixed sentiment buckets to gauge overall satisfaction levels.
How does Sentiment Analysis monitor brand perception and track sentiment trends?
Sentiment Analysis monitors brand perception by analyzing feedback across channels over time, tracking how sentiment shifts. With 25% intent weight on trend monitoring, it enables you to spot emerging issues, measure campaign impact, and understand how customer opinions evolve—critical for brand health assessment.
Can Sentiment Analysis extract sentiment about specific product features?
Yes. Sentiment Analysis performs aspect-based sentiment analysis to extract sentiment about specific features or aspects of products. Rather than labeling entire reviews as positive or negative, it identifies which product elements (price, quality, design, support) customers praise or criticize.
What approaches does Sentiment Analysis use for opinion mining?
Sentiment Analysis uses lexicon-based methods, machine learning classifiers, and deep learning models for opinion extraction and classification. Lexicon approaches match words against sentiment dictionaries; ML models learn patterns from labeled data; deep learning captures complex semantic relationships in text.
Is Sentiment Analysis available under an open-source license?
Yes. Sentiment Analysis is released under the MIT license, allowing free use, modification, and distribution for both commercial and private projects with minimal restrictions.
SKILL.md
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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
```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
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skills/sentiment-analysis/SKILL.md
skills/sentiment-analysis/scripts/scaffold-analysis.sh
skills/sentiment-analysis/templates/notebook-template.py