AI Sentiment Analyzer – README.md

🚀 Project Overview

The AI Sentiment Analyzer is a sophisticated web-based tool that leverages advanced Natural Language Processing algorithms to decode human communication patterns. Built as part of my MS Data Analytics Engineering portfolio at Northeastern University, this tool demonstrates practical applications of sentiment analysis, engagement detection, and communication style classification.

4

Analysis Categories

100+

Word Classifications

8

Communication Styles

500+

Lines of Code

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✨ Key Features

📊 Overall Sentiment Analysis

Analyzes positive, negative, and neutral sentiment using advanced word classification and contextual analysis.

🎯 Engagement Level Detection

Measures participation level through question frequency, response length, and follow-up indicators.

💬 Communication Style Classification

Identifies professional, urgent, enthusiastic, detailed, and other communication patterns.

🔍 Response Quality Analysis

Evaluates the depth, thoughtfulness, and quality of responses using multiple metrics.

🧠 Smart Insights Generation

Provides actionable insights and recommendations based on communication patterns.

📱 Real-time Processing

Instant analysis with smooth animations and professional user interface.

🛠️ Technology Stack

JavaScript ES6+ HTML5 CSS3 Natural Language Processing Sentiment Analysis Regular Expressions DOM Manipulation Responsive Design WordPress Integration

🔬 Algorithm Architecture

Core Processing Pipeline:

Text Preprocessing: Tokenization, normalization, and cleaning
Word Classification: Categorization into positive, negative, professional, and urgent terms
Pattern Recognition: Detection of questions, exclamations, emojis, and structural elements
Sentiment Scoring: Weighted calculation based on word frequencies and context
Engagement Analysis: Assessment of participation level and response quality
Style Classification: Determination of communication patterns and tone
Insight Generation: Production of actionable recommendations

📈 Sentiment Analysis Algorithm

function analyzeSentiment(text) {
    const positiveWords = [‘excellent’, ‘outstanding’, ‘fantastic’, …];
    const negativeWords = [‘terrible’, ‘disappointing’, ‘frustrated’, …];
    const professionalWords = [‘please’, ‘regards’, ‘schedule’, …];
    const urgentWords = [‘urgent’, ‘asap’, ‘critical’, …];
    
    // Word frequency analysis
    const words = text.toLowerCase().split(/\s+/);
    let scores = { positive: 0, negative: 0, professional: 0, urgent: 0 };
    
    words.forEach(word => {
        const cleanWord = word.replace(/[^\w]/g, ”);
        if (positiveWords.includes(cleanWord)) scores.positive++;
        if (negativeWords.includes(cleanWord)) scores.negative++;
        // … additional classifications
    });
    
    return calculateSentimentRatio(scores, words.length);
}
                

🎯 Engagement Level Calculation

function analyzeEngagementLevel(text, sentiment) {
    let engagementScore = 50; // Base neutral engagement
    
    // Factors influencing engagement
    engagementScore += sentiment.overall * 0.3;        // Sentiment impact
    engagementScore += sentiment.professional * 5;     // Professional tone
    engagementScore += questionCount * 8;              // Question frequency
    engagementScore += responseLength / 30;            // Response depth
    engagementScore += followUpIndicators * 10;        // Future planning
    
    return Math.max(0, Math.min(100, engagementScore));
}
                

🎯 Use Cases

Business Communication: Analyze email exchanges, team communications, and professional correspondence
Customer Support: Evaluate support ticket conversations and customer satisfaction levels
Product Reviews: Process customer feedback and product review sentiment
Social Media: Analyze social media interactions and community engagement
Research Applications: Support academic research in communication studies
Training & Development: Help improve communication skills through feedback analysis

📊 Performance Metrics

⚡ Processing Speed

Real-time analysis with <2 second response time for typical conversations

🎯 Accuracy Rate

High precision sentiment classification with contextual understanding

📱 Cross-Platform

Fully responsive design works on desktop, tablet, and mobile devices

♿ Accessibility

WCAG compliant with proper contrast ratios and semantic markup

🔮 Future Enhancements

Machine Learning Integration: Implement TensorFlow.js for more sophisticated pattern recognition
Multi-language Support: Extend analysis capabilities to Spanish, French, and other languages
API Development: Create RESTful API for integration with other applications
Historical Analysis: Add capability to track communication patterns over time
Export Functionality: Generate PDF reports and CSV data exports
Custom Training: Allow users to train models on domain-specific communication styles

🏆 Project Impact

This project demonstrates advanced skills in:

Natural Language Processing: Practical application of NLP techniques for real-world communication analysis
Algorithm Development: Custom sentiment analysis algorithms tailored for multiple communication contexts
User Experience Design: Intuitive interface with real-time feedback and professional visualizations
Full-Stack Development: End-to-end implementation from algorithm design to web deployment
Data Science Applications: Bridge between academic NLP concepts and practical business applications

🤖 AI Sentiment Analyzer