sentiment-analyzer

```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")

```

• 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

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")

```

```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 |

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]}")

```

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 |

```

textblob>=0.17.0

pandas>=2.0.0

matplotlib>=3.7.0

```

• 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