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

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

analyzer = SentimentAnalyzer()

Single Text Analysis

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

# 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

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

Visualizations

# 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

# 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

Examples

Product Review Analysis

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

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

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

{
  "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

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