Sentiment Analysis
Analyze the emotional tone and sentiment of text using multiple advanced models.
About: Sentiment analysis determines the emotional tone behind text to identify if it expresses positive, negative, or neutral sentiment.
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Text Processing
Analysis
Advanced NLP
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Sentiment Analysis Methods
VADER
Rule-based Analyzer
Rule-based sentiment analyzer specifically tuned for social media text with compound scoring.
Lexicon-based approach
Social media optimized
Fast and reliable
DistilBERT
Transformer Model
Transformer model fine-tuned on Stanford Sentiment Treebank dataset with high accuracy.
Deep learning approach
~91% accuracy
Context-aware
RoBERTa Emotion
Multi-label Emotion
Multi-label emotion detection model identifying specific emotions like joy, anger, sadness, etc.
Emotion classification
Multi-label detection
Detailed emotional analysis
Sentiment Scale
Negative
Score: -1.0 to -0.05
Neutral
Score: -0.05 to 0.05
Positive
Score: 0.05 to 1.0
Emotions
Joy, Anger, Sadness, Fear, etc.
Sentiment Analysis Results
Click "Analyze Sentiment" to see sentiment analysis results