Agent skill

sentiment-analyzer

Analyze text sentiment (positive/negative/neutral) with confidence scores, emotion detection, and visualization. Supports single text, CSV batch, and trend analysis.

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Install this agent skill to your Project

npx add-skill https://github.com/dkyazzentwatwa/chatgpt-skills/tree/main/sentiment-analyzer

SKILL.md

Sentiment Analyzer

Analyze the sentiment of text content with detailed scoring, emotion detection, and visualization capabilities. Process single texts, CSV files, or track sentiment trends over time.

Quick Start

python
from scripts.sentiment_analyzer import SentimentAnalyzer

# Analyze single text
analyzer = SentimentAnalyzer()
result = analyzer.analyze("I love this product! It's amazing.")
print(f"Sentiment: {result['sentiment']} ({result['score']:.2f})")

# Batch analyze CSV
results = analyzer.analyze_csv("reviews.csv", text_column="review")
analyzer.plot_distribution("sentiment_dist.png")

Features

  • Sentiment Classification: Positive, negative, neutral with confidence
  • Polarity Scoring: -1.0 (negative) to +1.0 (positive)
  • Subjectivity Detection: Objective vs subjective content
  • Emotion Detection: Joy, anger, sadness, fear, surprise
  • Batch Processing: Analyze CSV files with any text column
  • Trend Analysis: Track sentiment over time
  • Visualizations: Distribution plots, trend charts, word clouds

API Reference

Initialization

python
analyzer = SentimentAnalyzer()

Single Text Analysis

python
result = analyzer.analyze("This is great!")
# Returns:
# {
#     'text': 'This is great!',
#     'sentiment': 'positive',  # positive, negative, neutral
#     'score': 0.85,            # -1.0 to 1.0
#     'confidence': 0.92,       # 0.0 to 1.0
#     'subjectivity': 0.75,     # 0.0 (objective) to 1.0 (subjective)
#     'emotions': {'joy': 0.8, 'anger': 0.0, ...}
# }

Batch Analysis

python
# From list
texts = ["Great product!", "Terrible service.", "It's okay."]
results = analyzer.analyze_batch(texts)

# From CSV
results = analyzer.analyze_csv(
    "reviews.csv",
    text_column="review_text",
    output="results.csv"
)

Trend Analysis

python
# Analyze sentiment over time
results = analyzer.analyze_csv(
    "posts.csv",
    text_column="content",
    date_column="posted_at"
)
analyzer.plot_trend("sentiment_trend.png")

Visualizations

python
# Sentiment distribution
analyzer.plot_distribution("distribution.png")

# Sentiment over time
analyzer.plot_trend("trend.png")

# Word cloud by sentiment
analyzer.plot_wordcloud("positive", "positive_words.png")

CLI Usage

bash
# Analyze single text
python sentiment_analyzer.py --text "I love this product!"

# Analyze file
python sentiment_analyzer.py --input reviews.csv --column review --output results.csv

# With visualization
python sentiment_analyzer.py --input reviews.csv --column text --plot distribution.png

# Trend analysis
python sentiment_analyzer.py --input posts.csv --column content --date posted_at --trend trend.png

CLI Arguments

Argument Description Default
--text Single text to analyze -
--input Input CSV file -
--column Text column name text
--date Date column for trends -
--output Output CSV file -
--plot Save distribution plot -
--trend Save trend plot -
--format Output format (json, csv) json

Examples

Product Review Analysis

python
analyzer = SentimentAnalyzer()
results = analyzer.analyze_csv("amazon_reviews.csv", text_column="review")

# Summary statistics
positive = sum(1 for r in results if r['sentiment'] == 'positive')
negative = sum(1 for r in results if r['sentiment'] == 'negative')
print(f"Positive: {positive}, Negative: {negative}")

# Average sentiment score
avg_score = sum(r['score'] for r in results) / len(results)
print(f"Average sentiment: {avg_score:.2f}")

Social Media Monitoring

python
analyzer = SentimentAnalyzer()

# Analyze tweets with timestamps
results = analyzer.analyze_csv(
    "tweets.csv",
    text_column="tweet_text",
    date_column="created_at"
)

# Plot sentiment trend
analyzer.plot_trend("twitter_sentiment.png", title="Brand Sentiment Over Time")

Customer Feedback Categorization

python
analyzer = SentimentAnalyzer()

feedback = [
    "Your support team was incredibly helpful!",
    "The product broke after one day.",
    "Shipping was on time.",
    "I'm extremely disappointed with the quality.",
    "It works as expected, nothing special."
]

for text in feedback:
    result = analyzer.analyze(text)
    print(f"{result['sentiment'].upper():8} ({result['score']:+.2f}): {text[:50]}")

Output Format

JSON Output

json
{
  "text": "I love this product!",
  "sentiment": "positive",
  "score": 0.85,
  "confidence": 0.92,
  "subjectivity": 0.75,
  "emotions": {
    "joy": 0.82,
    "anger": 0.02,
    "sadness": 0.01,
    "fear": 0.03,
    "surprise": 0.12
  }
}

CSV Output

text sentiment score confidence subjectivity
Great product! positive 0.85 0.91 0.80
Terrible... negative -0.72 0.88 0.65

Dependencies

textblob>=0.17.0
pandas>=2.0.0
matplotlib>=3.7.0

Limitations

  • English language optimized (other languages may have reduced accuracy)
  • Sarcasm and irony may not be detected accurately
  • Context-dependent sentiment may be missed
  • Short texts (<5 words) have lower confidence

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