Real-Time Sentiment Analysis
An intelligent NLP tool that analyzes text sentiment in real-time, detecting emotions, extracting keywords, and providing confidence scores.
Enter Text to Analyze
Analysis Results
neutral
Confidence: 0.0%
Try Sample Texts
How Sentiment Analysis Works
- 1. Lexicon-Based: Uses a dictionary of positive and negative words
- 2. Context Detection: Handles negations (e.g., "not good") and intensifiers (e.g., "very bad")
- 3. Scoring: Calculates sentiment score from -1.0 (very negative) to +1.0 (very positive)
- 4. Confidence: Based on the density of sentiment words in the text
- 5. Real-Time: Analysis updates as you type, with no server needed
Key Features
- Real-Time Analysis: Sentiment updates instantly as you type
- Keyword Extraction: Identifies emotional words driving the sentiment
- Context Awareness: Handles negations and intensifiers correctly
- Confidence Scoring: Indicates how certain the analysis is
- Analysis History: Save and review recent analyses
Technical Implementation
NLP Techniques
- Lexicon-based sentiment classification
- Negation handling ("not good" = negative)
- Intensifier detection ("very", "extremely")
- Normalized scoring (-1.0 to +1.0)
- Confidence calculation based on word density
Technology Stack
- TypeScript for type safety
- React hooks for state management
- Custom NLP algorithm (no external APIs)
- Client-side processing (instant results)
Performance
- Instant analysis (no API delays)
- 100% privacy (data never leaves browser)
- Works offline
- 0€ operational cost
Real-World Applications
Social Media Monitoring
Analyze customer feedback, tweets, and reviews to understand public sentiment about products or brands.
Customer Support
Automatically categorize support tickets by sentiment to prioritize urgent negative feedback.
Market Research
Analyze survey responses and product reviews at scale to extract customer opinions and trends.
Industry Use Cases:
How the Algorithm Works
Step 1: Text Preprocessing
The text is tokenized into individual words, converted to lowercase, and cleaned of punctuation while preserving apostrophes for contractions.
Step 2: Lexicon Matching
Each word is checked against positive and negative sentiment dictionaries containing hundreds of emotional words.
Step 3: Context Analysis
Negations ("not", "never") flip sentiment polarity. Intensifiers ("very", "extremely") increase sentiment strength by 50%.
Step 4: Score Calculation
Positive and negative scores are summed, then normalized to a -1.0 to +1.0 scale. Confidence is based on sentiment word density.
Step 5: Classification
Texts with score > 0.1 are positive, < -0.1 are negative, and between are neutral. Keywords driving the sentiment are extracted and displayed.
Potential Enhancements
Advanced NLP:
- Machine learning model (BERT, RoBERTa)
- Multi-language support
- Emoji and emoticon detection
- Sarcasm detection
Additional Features:
- Emotion classification (joy, anger, sadness)
- Entity extraction (people, places, products)
- Topic modeling
- Batch analysis for multiple texts
This is a lightweight demo using lexicon-based analysis. For production applications, I can implement advanced ML-based sentiment analysis with higher accuracy.