# ================================================================================
# Sentiment Analysis Dataset - Python Dependencies
# 
# Project: Sentiment Analysis Dataset
# Author: Molla Samser (Founder)
# Designer & Tester: Rima Khatun
# Website: https://rskworld.in
# Email: help@rskworld.in | support@rskworld.in
# 
# © 2026 RSK World - Free Programming Resources & Source Code
# 
# Installation: pip install -r requirements.txt
# ================================================================================

# Core Dependencies (Required)
# These are part of Python standard library, no installation needed:
# - argparse
# - csv
# - json
# - os
# - re
# - string
# - collections
# - datetime
# - hashlib
# - typing

# Optional Dependencies for Enhanced Features
# Install these for full functionality:

# NLP Processing
nltk>=3.8.1                    # Natural Language Toolkit
spacy>=3.7.0                   # Industrial-strength NLP

# Sentiment Analysis
textblob>=0.17.1               # Simple NLP library
vaderSentiment>=3.3.2          # VADER sentiment analyzer

# Data Visualization
matplotlib>=3.8.0              # Plotting library
wordcloud>=1.9.2               # Word cloud generator
numpy>=1.24.0                  # Numerical computing

# Data Processing
pandas>=2.1.0                  # Data manipulation (optional)

# Machine Learning (Advanced)
scikit-learn>=1.3.0            # ML algorithms
# torch>=2.1.0                 # PyTorch (uncomment if needed)
# transformers>=4.35.0         # Hugging Face transformers (uncomment if needed)

# ================================================================================
# Quick Install Commands:
# 
# Basic (required for core functionality):
#   pip install nltk textblob vaderSentiment
# 
# Visualization:
#   pip install matplotlib wordcloud numpy
# 
# Full install:
#   pip install -r requirements.txt
# 
# Download NLTK data (run after install):
#   python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('wordnet')"
# 
# Download spaCy model (optional):
#   python -m spacy download en_core_web_sm
# ================================================================================