Advanced Student Feedback & Dashboard System Using Sentiment Analysis

Student Feedback & Dashboard System

Enhance the feedback system of your educational institution with our Advanced Student Feedback & Dashboard System Using Sentiment Analysis. This professional-grade web application helps automate and streamline student feedback using powerful machine learning algorithms and sentiment analysis techniques powered by NLTK.

Students can submit feedback anonymously on departments and faculty members. The system then analyzes the feedback and classifies sentiments as Positive (1), Neutral (0), or Negative (-1). The project also provides separate dashboards for departments and teachers, helping them understand and improve based on real insights.

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Project Details

Key Features

• Login Page for secure access to the system
• Anonymous Feedback Submission by students
• Sentiment Classification using NLTK and Machine Learning (Positive, Neutral, Negative)
• Department Feedback Dashboard with sentiment graphs and data insights
• Staff Feedback Dashboard for individual teachers to view feedback and improvement suggestions
• Feedback Classification Display for easy analysis
• Role-Based Access Control to protect sensitive information
• Feedback Storage & History View
• Machine Learning Integration to identify the best algorithm for sentiment prediction

About the Project

The main objective of this system is to automate and analyze student feedback using Natural Language Processing. Using sentiment analysis algorithms, feedback is automatically processed and categorized to assist educational departments and staff in understanding student perspectives and improving teaching methodologies.

• Machine Learning Algorithms Used: Naive Bayes, SVM, and others to determine the most accurate sentiment classification.
• NLTK Integration: For processing and cleaning feedback text before analysis.
• Data Visualization: Display sentiment analysis in an intuitive dashboard for staff and department-level users.

How It Works

1. Student logs in anonymously and submits feedback.
2. The system processes feedback using NLTK for cleaning and feature extraction.
3. Machine learning algorithms are applied to classify sentiments.
4. Dashboards for departments and individual teachers present the results.
5. Teachers get suggestions for improvement based on sentiment analysis.

Download & Run Instructions

1. Download the Complete Project ZIP File
2. Extract the files in your web server root (e.g., XAMPP/htdocs or Flask environment)
3. Configure MySQL Database (SQL file included)
4. Run the application using Flask commands or appropriate Python environment setup
5. Access the system via browser

Recommended Python Version: 3.8+
MySQL is required for data storage.