Best Student Feedback & Dashboard System Using Sentiment Analysis

Student Feedback & Dashboard System

This Student Feedback & Dashboard System is a web app that helps colleges and schools take feedback from students in a smart way. It uses machine learning and sentiment analysis (with NLTK) to check if the feedback is positive, neutral, or negative. Students can give feedback without their name on departments and teachers. The system checks the feedback and shows the result as Positive (1), Neutral (0), or Negative (-1). There are also dashboards for each teacher and department to help them understand what students think and improve things

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Project Details

Key Features

1. Login Page for secure access to the system
2. Anonymous Feedback Submission by students
3. Sentiment Classification using NLTK and Machine Learning (Positive, Neutral, Negative)
4. Department Feedback Dashboard with sentiment graphs and data insights
5. Staff Feedback Dashboard for individual teachers to view feedback and improvement suggestions
6. Feedback Classification Display for easy analysis
7. Role-Based Access Control to protect sensitive information
8. Feedback Storage & History View
9. 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.

1. Machine Learning Algorithms Used: Naive Bayes, SVM, and others to determine the most accurate sentiment classification.
2. NLTK Integration: For processing and cleaning feedback text before analysis.
3. 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.