RSK World - Sentiment Analysis Dataset - Project Files Browser | RSK World

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================================================================================
 * Sentiment Analysis Dataset - Demo Page
 * 
 * Project: Sentiment Analysis Dataset
 * Description: Text sentiment analysis dataset with labeled reviews, comments,
 *              and social media posts for sentiment classification models.
 * Category: Text Data
 * Difficulty: Intermediate
 * 
 * Author: Molla Samser (Founder)
 * Designer & Tester: Rima Khatun
 * Website: https://rskworld.in
 * Email: help@rskworld.in | support@rskworld.in
 * Phone: +91 93305 39277
 * 
 * © 2026 RSK World - Free Programming Resources & Source Code
 * All rights reserved.
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                    <span>Text Data</span>
                </div>
                <h1 class="hero-title">
                    Sentiment Analysis
                    <span class="gradient-text">Dataset</span>
                </h1>
                <p class="hero-description">
                    Text sentiment analysis dataset with labeled reviews, comments, and social media posts 
                    for sentiment classification models. Perfect for NLP applications and machine learning projects.
                </p>
                <div class="hero-stats">
                    <div class="stat-item">
                        <i class="fas fa-database"></i>
                        <span class="stat-value">50,000+</span>
                        <span class="stat-label">Data Points</span>
                    </div>
                    <div class="stat-item">
                        <i class="fas fa-tags"></i>
                        <span class="stat-value">3</span>
                        <span class="stat-label">Sentiment Classes</span>
                    </div>
                    <div class="stat-item">
                        <i class="fas fa-layer-group"></i>
                        <span class="stat-value">5</span>
                        <span class="stat-label">File Formats</span>
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                <div class="hero-actions">
                    <a href="./sentiment-analysis.zip" class="btn btn-primary" download>
                        <i class="fas fa-download"></i>
                        Download Full Dataset
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                    <a href="#explore" class="btn btn-secondary">
                        <i class="fas fa-eye"></i>
                        Explore Dataset
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            <div class="hero-visual">
                <div class="sentiment-showcase">
                    <div class="sentiment-card positive">
                        <div class="sentiment-icon">
                            <i class="fas fa-smile"></i>
                        </div>
                        <div class="sentiment-info">
                            <span class="sentiment-label">Positive</span>
                            <span class="sentiment-count">18,500+</span>
                        </div>
                    </div>
                    <div class="sentiment-card neutral">
                        <div class="sentiment-icon">
                            <i class="fas fa-meh"></i>
                        </div>
                        <div class="sentiment-info">
                            <span class="sentiment-label">Neutral</span>
                            <span class="sentiment-count">15,200+</span>
                        </div>
                    </div>
                    <div class="sentiment-card negative">
                        <div class="sentiment-icon">
                            <i class="fas fa-frown"></i>
                        </div>
                        <div class="sentiment-info">
                            <span class="sentiment-label">Negative</span>
                            <span class="sentiment-count">16,300+</span>
                        </div>
                    </div>
                </div>
            </div>
        </div>
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    <!-- Features Section -->
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            <div class="section-header">
                <span class="section-badge">Features</span>
                <h2 class="section-title">What's Included</h2>
                <p class="section-description">
                    Everything you need for sentiment analysis and NLP model training
                </p>
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            <div class="features-grid">
                <div class="feature-card">
                    <div class="feature-icon">
                        <i class="fas fa-tags"></i>
                    </div>
                    <h3>Labeled Sentiment Data</h3>
                    <p>Pre-labeled text data with positive, negative, and neutral sentiment classifications</p>
                </div>
                <div class="feature-card">
                    <div class="feature-icon">
                        <i class="fas fa-layer-group"></i>
                    </div>
                    <h3>Multiple Text Sources</h3>
                    <p>Diverse collection from product reviews, social media posts, and user comments</p>
                </div>
                <div class="feature-card">
                    <div class="feature-icon">
                        <i class="fas fa-random"></i>
                    </div>
                    <h3>Training & Test Sets</h3>
                    <p>Pre-split datasets ready for machine learning model training and evaluation</p>
                </div>
                <div class="feature-card">
                    <div class="feature-icon">
                        <i class="fas fa-broom"></i>
                    </div>
                    <h3>Preprocessed Versions</h3>
                    <p>Cleaned and tokenized versions ready for immediate use in NLP pipelines</p>
                </div>
                <div class="feature-card">
                    <div class="feature-icon">
                        <i class="fas fa-robot"></i>
                    </div>
                    <h3>Ready for NLP Models</h3>
                    <p>Compatible with NLTK, spaCy, and popular deep learning frameworks</p>
                </div>
                <div class="feature-card">
                    <div class="feature-icon">
                        <i class="fas fa-file-code"></i>
                    </div>
                    <h3>Multiple Formats</h3>
                    <p>Available in CSV, JSON, and TXT formats for maximum flexibility</p>
                </div>
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    <!-- Dataset Explorer Section -->
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                <span class="section-badge">Explore</span>
                <h2 class="section-title">Dataset Preview</h2>
                <p class="section-description">
                    Browse through sample data from our sentiment analysis collection
                </p>
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                    <i class="fas fa-globe"></i> All Samples
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                    <i class="fas fa-smile"></i> Positive
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                    <i class="fas fa-meh"></i> Neutral
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                    <i class="fas fa-frown"></i> Negative
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    <!-- Advanced Features Section -->
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            <div class="section-header">
                <span class="section-badge">🚀 Advanced</span>
                <h2 class="section-title">Advanced Features</h2>
                <p class="section-description">
                    Powerful Python scripts for data generation, analysis, and model training
                </p>
            </div>
            <div class="advanced-grid">
                <div class="advanced-card">
                    <div class="advanced-icon">
                        <i class="fas fa-magic"></i>
                    </div>
                    <div class="advanced-content">
                        <h3>Data Generator</h3>
                        <p>Generate unlimited synthetic sentiment data with customizable parameters</p>
                        <code>python generate_data.py --samples 10000</code>
                    </div>
                </div>
                <div class="advanced-card">
                    <div class="advanced-icon">
                        <i class="fas fa-broom"></i>
                    </div>
                    <div class="advanced-content">
                        <h3>Smart Preprocessor</h3>
                        <p>Clean, tokenize, and prepare text with lemmatization & stemming</p>
                        <code>python preprocess_data.py --lemmatize</code>
                    </div>
                </div>
                <div class="advanced-card">
                    <div class="advanced-icon">
                        <i class="fas fa-microscope"></i>
                    </div>
                    <div class="advanced-content">
                        <h3>Sentiment Analyzer</h3>
                        <p>Multi-method analysis using Lexicon, VADER, and TextBlob</p>
                        <code>python analyze_sentiment.py --interactive</code>
                    </div>
                </div>
                <div class="advanced-card">
                    <div class="advanced-icon">
                        <i class="fas fa-chart-pie"></i>
                    </div>
                    <div class="advanced-content">
                        <h3>Data Visualizer</h3>
                        <p>Generate charts, word clouds, and HTML reports automatically</p>
                        <code>python visualize_data.py --html-report</code>
                    </div>
                </div>
                <div class="advanced-card">
                    <div class="advanced-icon">
                        <i class="fas fa-brain"></i>
                    </div>
                    <div class="advanced-content">
                        <h3>Model Trainer</h3>
                        <p>Train ML models - Naive Bayes, SVM, Logistic Regression, Random Forest</p>
                        <code>python train_model.py --all-models --save</code>
                    </div>
                </div>
                <div class="advanced-card">
                    <div class="advanced-icon">
                        <i class="fas fa-sync-alt"></i>
                    </div>
                    <div class="advanced-content">
                        <h3>Cross-Validation</h3>
                        <p>Built-in cross-validation for reliable model evaluation</p>
                        <code>python train_model.py --cross-validate 5</code>
                    </div>
                </div>
            </div>
        </div>
    </section>

    <!-- Step by Step Guide Section -->
    <section class="guide" id="guide">
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            <div class="section-header">
                <span class="section-badge">📖 Tutorial</span>
                <h2 class="section-title">Step-by-Step Guide</h2>
                <p class="section-description">
                    Follow these steps to get started with the dataset and Python scripts
                </p>
            </div>
            <div class="guide-timeline">
                <div class="guide-step">
                    <div class="step-number">1</div>
                    <div class="step-content">
                        <h3>Download & Extract</h3>
                        <p>Download the dataset ZIP file and extract it to your project folder</p>
                        <div class="step-code">
                            <span class="code-label">Terminal</span>
                            <pre><code>unzip sentiment-analysis.zip
cd sentiment-analysis</code></pre>
                        </div>
                    </div>
                </div>
                <div class="guide-step">
                    <div class="step-number">2</div>
                    <div class="step-content">
                        <h3>Install Dependencies</h3>
                        <p>Install required Python packages for full functionality</p>
                        <div class="step-code">
                            <span class="code-label">Terminal</span>
                            <pre><code>cd scripts
pip install -r requirements.txt

# Download NLTK data
python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('wordnet')"</code></pre>
                        </div>
                    </div>
                </div>
                <div class="guide-step">
                    <div class="step-number">3</div>
                    <div class="step-content">
                        <h3>Generate Custom Data</h3>
                        <p>Create your own dataset with desired size and parameters</p>
                        <div class="step-code">
                            <span class="code-label">Terminal</span>
                            <pre><code># Generate 5000 balanced samples
python generate_data.py --samples 5000 --balanced

# Generate with train/test split (80/20)
python generate_data.py --samples 10000 --split 0.8 --all-formats</code></pre>
                        </div>
                    </div>
                </div>
                <div class="guide-step">
                    <div class="step-number">4</div>
                    <div class="step-content">
                        <h3>Preprocess Data</h3>
                        <p>Clean and prepare text data for NLP models</p>
                        <div class="step-code">
                            <span class="code-label">Terminal</span>
                            <pre><code># Basic preprocessing
python preprocess_data.py --input ../data/sentiment_data.csv

# Advanced: with lemmatization and stopword removal
python preprocess_data.py --input ../data/sentiment_data.csv \
    --lemmatize --remove-stopwords --build-vocab</code></pre>
                        </div>
                    </div>
                </div>
                <div class="guide-step">
                    <div class="step-number">5</div>
                    <div class="step-content">
                        <h3>Analyze Sentiment</h3>
                        <p>Test sentiment analysis on your own text or evaluate datasets</p>
                        <div class="step-code">
                            <span class="code-label">Terminal</span>
                            <pre><code># Interactive mode - type any text
python analyze_sentiment.py --interactive

# Evaluate accuracy on dataset
python analyze_sentiment.py --file ../data/test_data.csv --evaluate</code></pre>
                        </div>
                    </div>
                </div>
                <div class="guide-step">
                    <div class="step-number">6</div>
                    <div class="step-content">
                        <h3>Train ML Models</h3>
                        <p>Train and compare multiple machine learning models</p>
                        <div class="step-code">
                            <span class="code-label">Terminal</span>
                            <pre><code># Train all models and save the best one
python train_model.py --input ../data/sentiment_data.csv \
    --all-models --save --output ../models/

# Train specific model with cross-validation
python train_model.py --input ../data/sentiment_data.csv \
    --model svm --cross-validate 5</code></pre>
                        </div>
                    </div>
                </div>
                <div class="guide-step">
                    <div class="step-number">7</div>
                    <div class="step-content">
                        <h3>Visualize Results</h3>
                        <p>Generate charts, word clouds, and comprehensive reports</p>
                        <div class="step-code">
                            <span class="code-label">Terminal</span>
                            <pre><code># Generate all visualizations with HTML report
python visualize_data.py --input ../data/sentiment_data.csv \
    --all-charts --html-report --output ../charts/</code></pre>
                        </div>
                    </div>
                </div>
            </div>
        </div>
    </section>

    <!-- Unique Features Section -->
    <section class="unique-features" id="unique">
        <div class="container">
            <div class="section-header">
                <span class="section-badge">⭐ Unique</span>
                <h2 class="section-title">What Makes Us Different</h2>
                <p class="section-description">
                    Features you won't find in other sentiment analysis datasets
                </p>
            </div>
            <div class="unique-grid">
                <div class="unique-card featured">
                    <div class="unique-badge">Most Popular</div>
                    <div class="unique-icon">
                        <i class="fas fa-infinity"></i>
                    </div>
                    <h3>Unlimited Data Generation</h3>
                    <p>Generate 1,000 to 1,000,000+ samples with a single command. No limits!</p>
                    <ul class="unique-list">
                        <li><i class="fas fa-check"></i> Customizable sample count</li>
                        <li><i class="fas fa-check"></i> Balanced or custom distribution</li>
                        <li><i class="fas fa-check"></i> Multiple output formats</li>
                    </ul>
                </div>
                <div class="unique-card">
                    <div class="unique-icon">
                        <i class="fas fa-layer-group"></i>
                    </div>
                    <h3>Ensemble Analysis</h3>
                    <p>Combine 3 different sentiment analysis methods for maximum accuracy</p>
                    <ul class="unique-list">
                        <li><i class="fas fa-check"></i> Lexicon-based analyzer</li>
                        <li><i class="fas fa-check"></i> VADER sentiment</li>
                        <li><i class="fas fa-check"></i> TextBlob integration</li>
                    </ul>
                </div>
                <div class="unique-card">
                    <div class="unique-icon">
                        <i class="fas fa-robot"></i>
                    </div>
                    <h3>4 ML Algorithms</h3>
                    <p>Train and compare multiple machine learning models automatically</p>
                    <ul class="unique-list">
                        <li><i class="fas fa-check"></i> Naive Bayes</li>
                        <li><i class="fas fa-check"></i> Support Vector Machine</li>
                        <li><i class="fas fa-check"></i> Logistic Regression</li>
                        <li><i class="fas fa-check"></i> Random Forest</li>
                    </ul>
                </div>
                <div class="unique-card">
                    <div class="unique-icon">
                        <i class="fas fa-terminal"></i>
                    </div>
                    <h3>Interactive Mode</h3>
                    <p>Real-time sentiment analysis with instant feedback</p>
                    <ul class="unique-list">
                        <li><i class="fas fa-check"></i> Type any text to analyze</li>
                        <li><i class="fas fa-check"></i> See detailed scores</li>
                        <li><i class="fas fa-check"></i> Compare methods live</li>
                    </ul>
                </div>
                <div class="unique-card">
                    <div class="unique-icon">
                        <i class="fas fa-file-export"></i>
                    </div>
                    <h3>Auto Reports</h3>
                    <p>Generate beautiful HTML reports with one command</p>
                    <ul class="unique-list">
                        <li><i class="fas fa-check"></i> Sentiment distribution charts</li>
                        <li><i class="fas fa-check"></i> Word frequency analysis</li>
                        <li><i class="fas fa-check"></i> Word cloud generation</li>
                    </ul>
                </div>
                <div class="unique-card">
                    <div class="unique-icon">
                        <i class="fas fa-save"></i>
                    </div>
                    <h3>Model Persistence</h3>
                    <p>Save trained models and load them anytime</p>
                    <ul class="unique-list">
                        <li><i class="fas fa-check"></i> Save best model automatically</li>
                        <li><i class="fas fa-check"></i> Load for predictions</li>
                        <li><i class="fas fa-check"></i> Production ready</li>
                    </ul>
                </div>
            </div>
        </div>
    </section>

    <!-- Python Scripts Section -->
    <section class="scripts" id="scripts">
        <div class="container">
            <div class="section-header">
                <span class="section-badge">🐍 Python</span>
                <h2 class="section-title">Included Scripts</h2>
                <p class="section-description">
                    5 powerful Python scripts for complete sentiment analysis workflow
                </p>
            </div>
            <div class="scripts-grid">
                <div class="script-card">
                    <div class="script-header">
                        <div class="script-icon">
                            <i class="fas fa-database"></i>
                        </div>
                        <h3>generate_data.py</h3>
                    </div>
                    <p class="script-desc">Generate synthetic sentiment analysis data</p>
                    <div class="script-features">
                        <span><i class="fas fa-check-circle"></i> Custom sample count</span>
                        <span><i class="fas fa-check-circle"></i> Balanced distribution</span>
                        <span><i class="fas fa-check-circle"></i> Train/test split</span>
                        <span><i class="fas fa-check-circle"></i> Multiple formats</span>
                    </div>
                    <div class="script-usage">
                        <span class="usage-label">Quick Start:</span>
                        <code>python generate_data.py -n 5000 -b</code>
                    </div>
                </div>
                <div class="script-card">
                    <div class="script-header">
                        <div class="script-icon">
                            <i class="fas fa-filter"></i>
                        </div>
                        <h3>preprocess_data.py</h3>
                    </div>
                    <p class="script-desc">Clean and prepare text for NLP models</p>
                    <div class="script-features">
                        <span><i class="fas fa-check-circle"></i> Tokenization</span>
                        <span><i class="fas fa-check-circle"></i> Lemmatization</span>
                        <span><i class="fas fa-check-circle"></i> Stopword removal</span>
                        <span><i class="fas fa-check-circle"></i> Vocabulary builder</span>
                    </div>
                    <div class="script-usage">
                        <span class="usage-label">Quick Start:</span>
                        <code>python preprocess_data.py -i data.csv</code>
                    </div>
                </div>
                <div class="script-card">
                    <div class="script-header">
                        <div class="script-icon">
                            <i class="fas fa-search"></i>
                        </div>
                        <h3>analyze_sentiment.py</h3>
                    </div>
                    <p class="script-desc">Analyze sentiment with multiple methods</p>
                    <div class="script-features">
                        <span><i class="fas fa-check-circle"></i> Lexicon analyzer</span>
                        <span><i class="fas fa-check-circle"></i> VADER integration</span>
                        <span><i class="fas fa-check-circle"></i> TextBlob support</span>
                        <span><i class="fas fa-check-circle"></i> Interactive mode</span>
                    </div>
                    <div class="script-usage">
                        <span class="usage-label">Quick Start:</span>
                        <code>python analyze_sentiment.py -i</code>
                    </div>
                </div>
                <div class="script-card">
                    <div class="script-header">
                        <div class="script-icon">
                            <i class="fas fa-chart-bar"></i>
                        </div>
                        <h3>visualize_data.py</h3>
                    </div>
                    <p class="script-desc">Generate charts and visual reports</p>
                    <div class="script-features">
                        <span><i class="fas fa-check-circle"></i> Distribution charts</span>
                        <span><i class="fas fa-check-circle"></i> Word clouds</span>
                        <span><i class="fas fa-check-circle"></i> Histograms</span>
                        <span><i class="fas fa-check-circle"></i> HTML reports</span>
                    </div>
                    <div class="script-usage">
                        <span class="usage-label">Quick Start:</span>
                        <code>python visualize_data.py -i data.csv -r</code>
                    </div>
                </div>
                <div class="script-card">
                    <div class="script-header">
                        <div class="script-icon">
                            <i class="fas fa-graduation-cap"></i>
                        </div>
                        <h3>train_model.py</h3>
                    </div>
                    <p class="script-desc">Train and evaluate ML models</p>
                    <div class="script-features">
                        <span><i class="fas fa-check-circle"></i> 4 ML algorithms</span>
                        <span><i class="fas fa-check-circle"></i> Cross-validation</span>
                        <span><i class="fas fa-check-circle"></i> Model saving</span>
                        <span><i class="fas fa-check-circle"></i> Metrics report</span>
                    </div>
                    <div class="script-usage">
                        <span class="usage-label">Quick Start:</span>
                        <code>python train_model.py -i data.csv -a</code>
                    </div>
                </div>
            </div>
        </div>
    </section>

    <!-- Technologies Section -->
    <section class="technologies" id="tech">
        <div class="container">
            <div class="section-header">
                <span class="section-badge">Technologies</span>
                <h2 class="section-title">Compatible With</h2>
                <p class="section-description">
                    Designed to work seamlessly with popular NLP tools and frameworks
                </p>
            </div>
            <div class="tech-grid">
                <div class="tech-card">
                    <div class="tech-icon csv">
                        <i class="fas fa-file-csv"></i>
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                    <span>CSV</span>
                </div>
                <div class="tech-card">
                    <div class="tech-icon txt">
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                    <span>TXT</span>
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                    <div class="tech-icon json">
                        <i class="fas fa-file-code"></i>
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                    <span>JSON</span>
                </div>
                <div class="tech-card">
                    <div class="tech-icon nltk">
                        <i class="fas fa-language"></i>
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                    <span>NLTK</span>
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                <div class="tech-card">
                    <div class="tech-icon spacy">
                        <i class="fas fa-cogs"></i>
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                    <span>spaCy</span>
                </div>
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    <!-- Dataset Structure Section -->
    <section class="structure" id="structure">
        <div class="container">
            <div class="section-header">
                <span class="section-badge">Structure</span>
                <h2 class="section-title">Dataset Organization</h2>
                <p class="section-description">
                    Well-organized file structure for easy navigation and integration
                </p>
            </div>
            <div class="structure-content">
                <div class="file-tree">
                    <div class="tree-header">
                        <i class="fas fa-folder-open"></i>
                        <span>sentiment-analysis/</span>
                    </div>
                    <ul class="tree-list">
                        <li class="tree-item">
                            <i class="fas fa-folder"></i>
                            <span>data/</span>
                            <ul class="tree-sublist">
                                <li><i class="fas fa-file-csv"></i> sentiment_data.csv</li>
                                <li><i class="fas fa-file-code"></i> sentiment_data.json</li>
                                <li><i class="fas fa-file-alt"></i> sentiment_data.txt</li>
                                <li><i class="fas fa-file-csv"></i> train_data.csv</li>
                                <li><i class="fas fa-file-csv"></i> test_data.csv</li>
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                            <i class="fas fa-folder"></i>
                            <span>preprocessed/</span>
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                                <li><i class="fas fa-file-csv"></i> cleaned_data.csv</li>
                                <li><i class="fas fa-file-code"></i> tokenized_data.json</li>
                            </ul>
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                        <li class="tree-item">
                            <i class="fas fa-folder"></i>
                            <span class="highlight-folder">scripts/</span>
                            <ul class="tree-sublist">
                                <li><i class="fab fa-python"></i> generate_data.py</li>
                                <li><i class="fab fa-python"></i> preprocess_data.py</li>
                                <li><i class="fab fa-python"></i> analyze_sentiment.py</li>
                                <li><i class="fab fa-python"></i> visualize_data.py</li>
                                <li><i class="fab fa-python"></i> train_model.py</li>
                                <li><i class="fas fa-file-alt"></i> requirements.txt</li>
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                        </li>
                        <li class="tree-item">
                            <i class="fas fa-file-alt"></i>
                            <span>README.md</span>
                        </li>
                        <li class="tree-item">
                            <i class="fas fa-file"></i>
                            <span>LICENSE</span>
                        </li>
                    </ul>
                </div>
                <div class="structure-info">
                    <h3>File Descriptions</h3>
                    <div class="info-item">
                        <strong>sentiment_data.csv/json/txt</strong>
                        <p>Main dataset containing all labeled sentiment data</p>
                    </div>
                    <div class="info-item">
                        <strong>train_data.csv</strong>
                        <p>80% of data for training machine learning models</p>
                    </div>
                    <div class="info-item">
                        <strong>test_data.csv</strong>
                        <p>20% of data for testing and validation</p>
                    </div>
                    <div class="info-item">
                        <strong>cleaned_data.csv</strong>
                        <p>Preprocessed data with removed noise and special characters</p>
                    </div>
                    <div class="info-item">
                        <strong>tokenized_data.json</strong>
                        <p>Text data tokenized and ready for NLP models</p>
                    </div>
                    <div class="info-item highlight">
                        <strong>scripts/*.py</strong>
                        <p>Python scripts for data generation, preprocessing, analysis, visualization, and model training</p>
                    </div>
                </div>
            </div>
        </div>
    </section>

    <!-- Statistics Section -->
    <section class="statistics" id="stats">
        <div class="container">
            <div class="section-header">
                <span class="section-badge">Statistics</span>
                <h2 class="section-title">Dataset Insights</h2>
            </div>
            <div class="stats-grid">
                <div class="stats-card">
                    <div class="stats-chart">
                        <canvas id="sentimentChart"></canvas>
                    </div>
                    <h3>Sentiment Distribution</h3>
                </div>
                <div class="stats-card">
                    <div class="stats-chart">
                        <canvas id="sourceChart"></canvas>
                    </div>
                    <h3>Data Sources</h3>
                </div>
                <div class="stats-card metrics">
                    <h3>Key Metrics</h3>
                    <div class="metric-list">
                        <div class="metric-item">
                            <span class="metric-label">Total Samples</span>
                            <span class="metric-value" data-count="50000">0</span>
                        </div>
                        <div class="metric-item">
                            <span class="metric-label">Avg. Text Length</span>
                            <span class="metric-value" data-count="142">0</span>
                            <span class="metric-unit">chars</span>
                        </div>
                        <div class="metric-item">
                            <span class="metric-label">Vocabulary Size</span>
                            <span class="metric-value" data-count="28500">0</span>
                        </div>
                        <div class="metric-item">
                            <span class="metric-label">Languages</span>
                            <span class="metric-value">English</span>
                        </div>
                    </div>
                </div>
            </div>
        </div>
    </section>

    <!-- Use Cases Section -->
    <section class="use-cases" id="usecases">
        <div class="container">
            <div class="section-header">
                <span class="section-badge">Applications</span>
                <h2 class="section-title">Use Cases</h2>
                <p class="section-description">
                    Discover what you can build with this dataset
                </p>
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            <div class="cases-grid">
                <div class="case-card">
                    <div class="case-icon">
                        <i class="fas fa-brain"></i>
                    </div>
                    <h3>Sentiment Classification</h3>
                    <p>Train models to automatically classify text sentiment as positive, negative, or neutral</p>
                </div>
                <div class="case-card">
                    <div class="case-icon">
                        <i class="fas fa-chart-bar"></i>
                    </div>
                    <h3>Brand Monitoring</h3>
                    <p>Analyze customer feedback and social media mentions for brand perception insights</p>
                </div>
                <div class="case-card">
                    <div class="case-icon">
                        <i class="fas fa-comments"></i>
                    </div>
                    <h3>Customer Reviews Analysis</h3>
                    <p>Automatically process and categorize product reviews at scale</p>
                </div>
                <div class="case-card">
                    <div class="case-icon">
                        <i class="fas fa-graduation-cap"></i>
                    </div>
                    <h3>NLP Education</h3>
                    <p>Learn and practice natural language processing techniques</p>
                </div>
            </div>
        </div>
    </section>

    <!-- Download Section -->
    <section class="download" id="download">
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            <div class="download-card">
                <div class="download-content">
                    <h2>Ready to Get Started?</h2>
                    <p>Download the complete sentiment analysis dataset and start building your NLP models today!</p>
                    <div class="download-meta">
                        <span><i class="fas fa-weight-hanging"></i> ~5 MB</span>
                        <span><i class="fas fa-file-archive"></i> ZIP Format</span>
                        <span><i class="fas fa-shield-alt"></i> No Registration</span>
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                    <a href="./sentiment-analysis.zip" class="btn btn-primary btn-large" download>
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                        Download Dataset
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                    <span class="download-note">Free for educational purposes</span>
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