Japanese NLP Sentiment Analysis of Student Project Feedback
Project Overview
This project applies a deep learning–based natural language processing approach to analyze Japanese student feedback collected via Google Forms from project-based English classes, using a pretrained Japanese BERT model.
The analysis focuses on identifying students' favorite and least favorite projects, reasons for their preferences, and suggestions for improvement. The goal is to extract actionable insights to enhance curriculum design and student engagement.
An earlier exploratory version (2023) focused on morphological analysis and keyword extraction. In 2025, the project was redesigned to use Japanese Sentence-BERT embeddings and unsupervised clustering to identify latent semantic themes in improvement feedback.
Key Findings
Project Popularity
- Most favored: Country Presentation, Skit Festival
- Least favored: Demonstration Dialogue
- Skit Festival appears frequently in both categories, suggesting polarized experiences.
Reasons for Favorite Projects
- Cultural learning opportunities
- Creative expression
- Autonomous and exploratory work
Areas for Improvement
- Time management constraints
- Project difficulty calibration
- Clearer instructions and expectations
Percentage Distribution of Favorite and Least Favorite Projects
Word Cloud Analysis
Favorite Project Reasons
Improvement Suggestions
BERT-Based Semantic Clustering of Improvement Feedback
Identified Clusters and Themes
| Cluster | Semantic Theme | Description |
|---|---|---|
| Cluster 1 | Practice Time & Preparation | Requests for longer rehearsal periods and collaboration time. |
| Cluster 0 | Task Structure & Instruction Clarity | Need for clearer instructions, examples, and constraints. |
| Cluster -1 | Miscellaneous Feedback | Low-frequency or project-specific comments. |
Cluster Distribution by Project
| Favorite Project | Misc. | Structure | Practice |
|---|---|---|---|
| Demonstration Dialogue | 4 | 0 | 0 |
| Country Presentation | 36 | 11 | 3 |
| Skit Festival | 37 | 8 | 5 |
| Interview Speech | 4 | 7 | 1 |
Technical Summary
Data Science Skills
- Natural Language Processing
- Data Cleaning & Analysis
- Visualization
Programming
- Python
- Jupyter Notebook
- Google Colab
Libraries & Tools
- Pandas
- Matplotlib & Seaborn
- Hugging Face Transformers
View the Project
View Source Code
Data Privacy Notice
All student responses were anonymized and analyzed in aggregate following institutional privacy guidelines.