User Experience Topic Modeling

Introduction

This AI-powered solution transforms how educational institutions analyze student feedback by automatically extracting meaningful topics from course evaluations and user comments. Using advanced Natural Language Processing techniques, the system identifies key themes, sentiment patterns, and areas of concern, enabling educators to make data-driven improvements to their courses and teaching methods.

Project Context: This demo is part of an ongoing collaboration between LINKS and the United Nations System Staff College (UNSSC), which provides training courses for UN staff and relevant partners worldwide. The project aims to analyze user feedback from UNSSC courses to enhance the quality and effectiveness of UN training programs, ultimately supporting better decision-making and capacity building within the United Nations system.

Key Features

• Interactive Data Upload: Upload CSV files or edit data directly in the interface with course IDs and user feedback
• Flexible Topic Modeling: Choose between LDA (Latent Dirichlet Allocation) for detailed topic extraction or Word Cloud for visual text analysis
• Course-Specific Analysis: Analyze feedback for specific courses or across all courses simultaneously
• Customizable Parameters: Adjust the number of topics (k) and add custom stop words to refine analysis
• Real-time Visualization: Generate interactive plots showing top 5 words per topic with their weights

Technologies Used

• Machine Learning: Scikit-learn for LDA topic modeling and text vectorization using CountVectorizer with configurable parameters
• Natural Language Processing (NLP): NLTK for text preprocessing, tokenization, lemmatization, and stop word removal
• Visualization: Plotly for interactive topic visualization and WordCloud for generating visual text representations
• Web Interface: Gradio for creating an intuitive, user-friendly demo interface with real-time analysis capabilities

Use Cases

UN Training Program Enhancement: As part of the LINKS-UNSSC collaboration, this solution analyzes feedback from United Nations staff and partners attending UNSSC courses. By automatically identifying recurring themes in course evaluations, the system helps UNSSC improve their training offerings, ensuring that UN personnel receive the most effective and relevant professional development opportunities.

Educational Quality Improvement: Universities and online learning platforms can automatically analyze thousands of student feedback comments to identify recurring themes such as “difficult concepts,” “unclear instructions,” or “helpful examples.” This enables course coordinators to quickly pinpoint areas needing improvement without manually reading through extensive feedback, leading to more targeted course enhancements and improved student satisfaction.

Corporate Training Analysis: Companies can use this solution to analyze employee feedback from training programs, identifying which topics resonate well and which need improvement, ultimately optimizing their workforce development strategies.

Live Demo

Input

The system expects a CSV file with two columns: course_id (course identifier) and user_feedback (text feedback from students). Alternatively, users can directly edit the sample data provided in the interactive dataframe component.

How it works

1. Data Processing: The system preprocesses text by removing punctuation, converting to lowercase, tokenizing, and applying lemmatization
2. Stop Word Filtering: Common words and user-defined stop words are removed to focus on meaningful content
3. Topic Extraction: LDA algorithm identifies latent topics by analyzing word co-occurrence patterns across documents
4. Visualization: Results are presented as interactive bar charts showing the top 5 words per topic with their statistical weights

Output

• Topic Analysis: For each of the k topics, displays the 5 most representative words with their importance weights
• Interactive Visualizations: Horizontal bar charts showing word weights for easy interpretation
• Word Clouds: Visual representation of word frequency across selected courses or entire dataset

Try it out

Try out the solution in real time on Hugging Face Spaces:

👉 Launch Demo

For deeper technical insights and code exploration, visit the GitHub repository.

Benefits

• Increased Efficiency: Automatically process hundreds of feedback comments in seconds instead of hours of manual review
• Time Savings: Reduce analysis time from days to minutes, enabling faster response to student concerns
• Cost Optimization: Minimize human resources needed for feedback analysis while maintaining high-quality insights
• Improved Accuracy: Eliminate human bias and ensure consistent topic identification across large datasets
• Actionable Insights: Transform unstructured feedback into clear, prioritized improvement areas

Integration

The solution can be easily integrated into existing Learning Management Systems (LMS) through its API endpoints or by incorporating the topic modeling pipeline into automated feedback processing workflows. Educational institutions can connect their student information systems to automatically analyze feedback after each course completion, generating regular reports for academic quality assurance teams. The modular design allows for customization of preprocessing steps, topic modeling parameters, and output formats to match specific institutional requirements.