RSK World - Sentiment Analysis Dataset | NLP | Text Classification | Machine Learning | RSK World

Text Classification NLP Machine Learning 3 Classes Python CSV/JSON

This project features a comprehensive Sentiment Analysis dataset designed for professional NLP, text classification, and machine learning applications. The dataset includes 50,000+ labeled text samples across 3 sentiment classes: Positive, Neutral, and Negative. Features diverse text sources from product reviews, social media posts, and customer feedback. Pre-split into Training (80%) and Test (20%) sets. Available in CSV, JSON, and TXT formats. Includes powerful Python scripts: generate_data.py for unlimited synthetic data generation, preprocess_data.py for text cleaning with lemmatization & stemming, analyze_sentiment.py for multi-method sentiment analysis (Lexicon, VADER, TextBlob), visualize_data.py for charts and word clouds, and train_model.py for ML model training (Naive Bayes, SVM, Logistic Regression, Random Forest). Compatible with NLTK, spaCy, TextBlob, VADER, and scikit-learn. The package includes interactive demo website with Chart.js analytics, comprehensive README.md, RELEASE_NOTES.md version history, and MIT License. Perfect for data scientists, researchers, students, and developers working on sentiment classification, brand monitoring, customer review analysis, and NLP education projects.

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Dataset Overview

Complete sentiment analysis dataset with 50,000+ labeled text samples across 3 sentiment classes for NLP and machine learning.

50,000+ labeled text samples
3 sentiment classes
Positive, Neutral, Negative
Product reviews included
Social media posts included
Customer feedback samples
Pre-split: Train (80%), Test (20%)
Average text length: 142 characters
Balanced class distribution
Clean, high-quality labels
Perfect for NLP & ML training

Dataset Structure & Files

Well-organized folder structure with training and test splits plus comprehensive preprocessed data files.

data/sentiment_data.csv - Main dataset
data/sentiment_data.json - JSON format
data/sentiment_data.txt - Plain text format
data/train_data.csv - Training set (80%)
data/test_data.csv - Test set (20%)
preprocessed/cleaned_data.csv - Cleaned text
preprocessed/tokenized_data.json - Tokenized data
scripts/ - Python utilities
Consistent naming convention
Easy to load with pandas/NLTK
Ready for immediate use

Machine Learning Training

Complete training pipeline with support for 4 ML algorithms and cross-validation for reliable evaluation.

Naive Bayes classifier
Support Vector Machine (SVM)
Logistic Regression
Random Forest classifier
TF-IDF vectorization
Count vectorization
Cross-validation support
Model checkpointing
Performance metrics report
Best model auto-selection
Model export & persistence

Multiple File Formats

Dataset available in multiple formats for maximum compatibility with different NLP tools and frameworks.

CSV format (.csv files)
JSON format with metadata
Plain text format (.txt)
Pandas DataFrame ready
NLTK corpus compatible
spaCy pipeline ready
Easy format conversion
Unicode text support
UTF-8 encoding
Comment lines for metadata
Header row included

Analysis & Visualization

Comprehensive analysis tools with multiple sentiment analysis methods and visualization capabilities.

Lexicon-based analysis
VADER sentiment analyzer
TextBlob integration
Ensemble analysis method
Sentiment distribution charts
Word frequency analysis
Word cloud generation
Text length histogram
Source distribution pie chart
HTML report generation
Export visualization images

Compatible Frameworks

Works with all major NLP frameworks and libraries out of the box.

NLTK (Natural Language Toolkit)
spaCy industrial NLP
TextBlob sentiment analysis
VADER sentiment analyzer
scikit-learn ML library
pandas data manipulation
matplotlib visualization
wordcloud generation
TensorFlow/Keras ready
PyTorch compatible
Jupyter Notebook support

What You Get

Complete package with all files needed for professional sentiment analysis projects.

50,000+ labeled text samples
5 Python utility scripts
generate_data.py - Data generator
preprocess_data.py - Text preprocessor
analyze_sentiment.py - Sentiment analyzer
visualize_data.py - Data visualizer
train_model.py - Model trainer
Interactive demo website
Comprehensive README.md
RELEASE_NOTES.md version history
MIT License included

Interactive Demo Website

Beautiful demo website with dataset explorer, filtering, analytics dashboard, and step-by-step guide.

Modern animated design
Dataset preview explorer
Filter by sentiment class
Real-time sample browsing
Sentiment card visualization
Chart.js analytics dashboard
Dataset statistics display
Step-by-step usage guide
Python scripts documentation
Dark theme with gradients
Fully responsive layout

Python Scripts Included

Professional Python scripts for data generation, preprocessing, analysis, visualization, and model training.

generate_data.py - Unlimited data generation
preprocess_data.py - Text cleaning & tokenization
analyze_sentiment.py - Multi-method analysis
visualize_data.py - Charts & word clouds
train_model.py - ML model training
requirements.txt - Dependencies list
Command-line interface
Interactive mode support
Well-documented code
Type hints included
Easy to customize

Sentiment Classes

3 distinct sentiment classes covering the full spectrum of text sentiment for comprehensive classification.

Positive - Happy, satisfied, enthusiastic (18,500+ samples)
Neutral - Objective, balanced, informational (15,200+ samples)
Negative - Unhappy, disappointed, critical (16,300+ samples)
Product review sentiments
Social media post sentiments
Customer feedback sentiments
Clear labeling criteria
Human-verified labels
Balanced distribution option
Easy to extend classes
Total: 50,000+ samples

Credits & Acknowledgments

This dataset is provided for educational and research purposes. Core technologies and libraries are credited below.

Python 3.8+ - Programming language (PSF License)
NLTK - Natural Language Toolkit (Apache 2.0)
spaCy - Industrial NLP (MIT License)
TextBlob - Sentiment Analysis (MIT License)
VADER - Sentiment Analysis (MIT License)
scikit-learn - Machine Learning (BSD License)
matplotlib - Data Visualization (PSF License)
RSK World - Dataset creator and provider
GitHub Repository - Source code and releases
Author: Molla Samser | Designer: Rima Khatun
MIT License - Free for learning & research

Support & Contact

For commercial use, custom datasets, or integration help, please contact us.

Email: help@rskworld.in
Phone: +91 93305 39277
Website: RSKWORLD.in
Location: Nutanhat, Mongolkote, West Bengal, India
Author: Molla Samser
Designer & Tester: Rima Khatun
GitHub: Coming Soon
Sentiment Analysis Dataset Documentation
Technical Support Available
Custom Dataset Requests Welcome

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Sentiment Analysis

NLTK

Text Classification

scikit-learn

Python

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Sentiment Analysis Dataset

Dataset Overview

Dataset Structure & Files

Machine Learning Training

Multiple File Formats

Analysis & Visualization

Compatible Frameworks

What You Get

Interactive Demo Website

Python Scripts Included

Sentiment Classes

Credits & Acknowledgments

Support & Contact

Featured Content

Additional Sponsored Content

Download Free Source Code

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