Interactive NLP Learning Platform for Beginners

🔗 Live Demo

ThinkNLP is an educational web application designed to help beginners in Natural Language Processing (NLP) understand the full pipeline of sentiment and topic analysis using real-world review data. It provides a step-by-step, no-code interface to interactively explore how NLP models work.

• Full NLP pipeline walkthrough
• Upload and process real review data
• Compare sentiment models (VADER, TextBlob, BERT)
• Topic modeling with LDA and interactive visualization (pyLDAvis)
• Manual and auto topic labeling
• Sentiment distribution per topic
• Beginner-friendly UI with visual explanations

1. Upload Review File

   • Supports CSV input, stored in AWS S3 (gzip compressed)

2. Data Cleaning

   • Normalization: Lowercasing, typo correction
   • Special character removal: special character, number, and emoji
   • Tokenization: Breaking sentences into words
   • Stopword Removal: remove common words that not provide useful context
   • Lemmatization: Converting words to their base forms

3. EDA (Exploratory Data Analysis)

   • Word clouds, frequency charts, sentence length plots

4. Topic Modeling

   • LDA with auto or manual topic count
   • pyLDAvis visualization

5. Topic Labeling

   • Manual, keyword-based, or auto-inferred labels

6. Sentiment Analysis

   • VADER: Rule-based model
   • TextBlob: Lexicon-based
   • BERT: Transformer-based classifier (optional)

7. Sentiment-Topic Mapping

   • Each sentence assigned a dominant topic
   • Sentiment computed per topic
   • Output: Sentiment distribution per topic (Positive, Neutral, Negative)

• Frontend: React + TanStack Query (Vercel)
• Backend: FastAPI + PostgreSQL (Docker, DigitalOcean)
• Storage: AWS S3 for file uploads
• Monitoring: BetterStack for logs and metrics

_{Diagram illustrating request flow, infrastructure, CI/CD, and observability for ThinkNLP.}

• Gzip file compression
• Rate limiting & security headers
• Future: Background processing with Celery

• ✅ User authentication & file history
• ✅ Background task support (Celery + status UI)
• ✅ Expanded model selection and interpretability features
• ✅ Beginner tutorials and automatic result summaries
• ✅ Support with Multiple Languages beside English Language

• NLP beginners and students
• Educators and instructors
• Developers interested in NLP and no-code tools

• Docker
• Python 3.11

Create a Python virtual environment if you haven’t already:

python -m venv .venv
source .venv/bin/activate

Then run:

cd root_project
cp .env.example .env
make migrate        # Setup initial database schema
make up-local       # Run the backend server

Other helpful Makefile commands:

make reset-all      # Reset database and volumes
make logs-local     # View backend logs
make lint           # Run linter (ruff)
make test           # Run backend tests with coverage
...                 # You can check more on Makefile file

git clone https://github.com/sokritha-dev/think-nlp-frontend.git
cd root_project
yarn add
yarn dev

think-nlp/
├── .github/workflows/        # GitHub Actions workflows
├── .vscode/                  # VSCode editor settings
├── app/                      # FastAPI application code
├── k8s/                      # Kubernetes manifests
├── metric/                   # Monitoring & metrics utilities
├── migrations/               # Alembic migration files
├── reports/                  # Load test and analysis reports
├── scripts/                  # Helper and automation scripts
├── .autoenv.zsh              # Autoenv activation for Zsh
├── .dockerignore             # Docker ignore rules
├── .env.sample               # Example environment variables
├── .gitignore                # Git ignore rules
├── .python-version           # Python version pinning
├── Dockerfile                # Production Dockerfile
├── Dockerfile.dev            # Development Dockerfile
├── LICENSE                   # MIT License
├── Makefile                  # CLI automation for dev/test/deploy
├── alembic.ini               # Alembic configuration
├── docker-compose.*.yml      # Docker Compose files for different envs
├── locustfile.py             # Locust load testing script
├── pytest.ini                # Pytest config
├── requirements.txt          # Production dependencies
├── requirements-dev.txt      # Development dependencies

This project is licensed under the MIT License.

• Built using FastAPI, React, and pyLDAvis
• NLP components inspired by open-source models and tutorials