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':

}

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 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 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 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