Sentiment Analysis Tool
About This Project
A natural language processing tool that analyzes text from social media posts, reviews, or comments to determine sentiment (positive, negative, neutral). Includes data visualization, batch processing, and API access for integration.
Key Features
- Real-time text sentiment analysis
- Batch processing of CSV/text files
- Sentiment visualization dashboard
- Word cloud generation
- Historical sentiment tracking
- REST API for integration
- Support for multiple languages
- Export results to CSV/PDF
How It's Built
Collect Training Data
Use labeled sentiment datasets (Twitter sentiment, Amazon reviews). Preprocess text data (tokenization, stemming, stopword removal).
Build the ML Model
Train a sentiment classification model using TF-IDF and Logistic Regression or LSTM neural networks.
Create the Flask API
Build REST endpoints for single text analysis, batch processing, and model information.
Build the Frontend
Create a React interface with text input, results display, and visualization charts.
Add Data Visualization
Use Chart.js or D3.js to display sentiment distribution, word clouds, and trend analysis.
Deploy and Document
Deploy the API and frontend. Create API documentation for integration with other applications.
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