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ExamEase is an AI tool leveraging BERT for sentiment analysis, tailored to understand student sentiments during exams. It efficiently classifies emotions as positive, negative, or neutral, promoting a holistic view of student well-being.

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ExamEase

Description:

ExamEase is an AI-driven sentiment analysis tool designed specifically for recognizing student sentiments during exams. Utilizing the BERT model, it classifies student emotions into positive, negative, or neutral categories, providing a comprehensive understanding of student well-being.

Features:

  • BERT Model Integration: Advanced sentiment analysis using BERT.
  • Tri-sentiment Classification: Recognizes positive, negative, and neutral sentiments.
  • User-Friendly Interface: Easy-to-use command-line interface, and local hosting using Flask.
  • Focus on Well-being: Aims to understand and cater to student emotional needs during exams.

Installation:

  1. Clone the repository:
git clone https://github.com/yourusername/ExamEase.git
  1. Navigate to the directory:
cd ExamEase
  1. Install requirements:
pip install -r requirements.txt

Usage:

  1. Train the model:
python3 train_bert.py
  1. Run either in the terminal or on a local webpage
    1. Terminal:
      python3 main.py
    2. Local Host:
      python3 app.py
    open the webpage in your browser, generally its http://127.0.0.1:5000

Contributions:

Contributions are warmly welcomed! Feel free to create pull requests or open issues to discuss potential modifications or enhancements.

Disclaimer:

ExamEase is primarily a research and educational tool, serving as no substitute for professional medical or psychological advice, diagnosis, or treatment. For any specific health-related concerns or uncertainties, consulting a qualified healthcare professional is strongly advised. The developers and contributors of ExamEase assume no liability for any inaccuracies, misinterpretations, or misuse of the tool.

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ExamEase is an AI tool leveraging BERT for sentiment analysis, tailored to understand student sentiments during exams. It efficiently classifies emotions as positive, negative, or neutral, promoting a holistic view of student well-being.

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