EmotionIQ – AI-Powered Sentiment Analysis

EmotionIQ is a Flask-based web application that performs sentiment analysis on text input, uploaded files (TXT, PDF, DOCX), or web pages. It uses TextBlob and VADER to provide a reliable sentiment classification with confidence levels and emotion mapping. Users can also download a detailed PDF report of the analysis.

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Features

• Multiple Input Sources

  • Direct text entry
  • File uploads (.txt, .pdf, .docx)
  • Website URLs

• Multimodal Sentiment Detection

  • TextBlob polarity and subjectivity
  • VADER compound score
  • Combined confidence calculation

• Emotion Mapping

  • Positive → Joy / Satisfaction
  • Negative → Anger / Sadness
  • Neutral → Calm / Neutral

• PDF Report Generation

  • Detailed sentiment report with preview, word & character counts
  • Downloadable for sharing or documentation

• Real-Time Analysis

  • Instant feedback via Flask session management

• Secure File Handling

  • Only allows TXT, PDF, DOCX
  • Files automatically deleted after processing

• CORS Support

  • Ready for frontend integration or external calls

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Notes on URL Analysis

• When analyzing website content, EmotionIQ scrapes raw text from the page using requests and BeautifulSoup.

• To prevent overly long processing, the text is sliced to the first ~5000 characters if large.

  • This is why char_count often appears around 5000 for web pages.

• Scripts, styles, and HTML elements are removed for cleaner sentiment analysis.

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Requirements

• Python 3.8+
• Flask, Flask-CORS
• TextBlob, NLTK (punkt, stopwords)
• VADER Sentiment (vaderSentiment)
• PyPDF2, python-docx
• Requests, BeautifulSoup4
• ReportLab

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Installation

1. Clone the repository

git clone https://github.com/jayeshgit65/EmotionIQ-AI-Powered-Sentiment-Analysis.git
cd EmotionIQ-AI-Powered-Sentiment-Analysis

2. Install dependencies

pip install -r requirements.txt

3. Download NLTK data (once)

python -m nltk.downloader punkt stopwords

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Usage

1. Start the Flask server

python app.py

2. Open in browser

http://localhost:5000

3. Perform analysis

• Enter text directly
• Upload a .txt, .pdf, or .docx file
• Enter a website URL

4. View results

• Sentiment, emotion, confidence, word count, character count
• Text preview and confidence bar chart

5. Download PDF Report

• Click "Download PDF Report" to save the analysis

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Project Structure

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├── app.py              # Main Flask application
├── templates/
│   ├── index.html      # Home page input UI
│   └── results.html    # Results page with sentiment display and chart
├── uploads/            # Temporary file uploads (auto-cleaned)
├── requirements.txt    # Python dependencies
└── README.md           # Project documentation

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API Endpoints

• GET / – Home page
• POST / – Process sentiment analysis from text, file, or URL
• GET /results – View sentiment analysis results
• GET /download – Download sentiment analysis report as PDF

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License

MIT License