bdstar/twitter-sentiment-analysis

🐦 Twitter Sentiment Analysis (bdstar/twitter-sentiment-analysis)

🧠 Overview

A refined and merged version of Twitter text sentiment datasets, providing a clean and well-balanced dataset for sentiment classification across three sentiment categories: `positive`, `negative`, and `neutral`.

This dataset is split into three parts — train, test, and validation — each sourced from highly reputable open datasets. It is designed for training, evaluating, and benchmarking NLP models for Twitter Sentiment Analysis and other social media text classification tasks.

🗂️ Dataset Splits

🧩 Column Descriptions

📊 Dataset Summary

📈 Detailed Statistics

🏋️‍♂️ Train Set

Source: Twitter Sentiment Dataset (3M labeled rows) File Size: 361 MB Rows: 3,142,209

🧪 Test Set

Source: Sentiment140 File Size: 198 MB Rows: 1,600,001

🧭 Validation Set

Source: MTEB – Tweet Sentiment Extraction File Size: 3.45 MB Rows: 31,015

💡 Usage Example (Python)

from datasets import load_dataset

# Load dataset from Hugging Face
dataset = load_dataset("bdstar/twitter-sentiment-analysis")

# Access splits
train = dataset["train"]
test = dataset["test"]
validation = dataset["validation"]

# Display sample
print(train[0])

🏷️ Citation

If you use this dataset in your research or application, please cite as:

@dataset{bdstar2025twitter,
  title        = {Twitter Sentiment Analysis (Refined Dataset)},
  author       = {Md Abdullah Al Mamun},
  year         = {2025},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/datasets/bdstar/twitter-sentiment-analysis}
}

📬 Contact

For questions, improvements, or collaboration: Author: Md Abdullah Al Mamun 📧 Email: mamunbd.ruet@gmail.com 🌐 Website: TechNTuts