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bdstar/Tweets-Sentiment-Analysis

🐦 Tweets-Sentiment-Analysis (bdstar/Tweets-Sentiment-Analysis) 🧠 Overview A refined and merged version of Tweets 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… See the full description on the dataset page: https://huggingface.co/datasets/bdstar/Tweets-Sentiment-Analysis.

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🐦 Tweets-Sentiment-Analysis (bdstar/Tweets-Sentiment-Analysis)

🧠 Overview

A refined and merged version of Tweets 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 Tweets Sentiment Analysis and other social media text classification tasks.

🗂️ Dataset Splits

#	Split	Name	Negative	Neutral	Positive	% Negative	% Neutral	% Positive	Total
1	Train	Sentiment140 (positive-sentence)	71,462	233,345	483,261	9.067999	29.609754	61.322246	788,068
2	Train	Sentiment140 (negative-sentence)	451,341	191,650	136,801	57.879665	24.577067	17.543268	779,792
3	Train	DailyDialog	12,623	45,674	20,226	16.075545	58.166397	25.758058	78,523
4	Test	ChatGPT Tweets Sentiment Analysis	194,425	360,060	295,108	22.884487	42.380293	34.735220	849,593
5	Validation	mteb-tweetsentimentextraction	10,083	7,969	12,070	33.473873	26.455747	40.070380	30,122
	Total	—	739,934	838,698	947,466	29.291579	33.201325	37.507096	2,526,098

The possiblity value of Negative, Positive and Neutral for a text has been calculated by the model cardiffnlp/twitter-roberta-base-sentiment-latest

🧩 Column Descriptions

Column	Type	Description
ID	Integer	Auto-incremental unique ID for each row
text	String	Tweet text content
negative	Float	Possiblity the text be a negative
neutral	Float	Possiblity the text be a neutral
positive	Float	Possiblity the text be a positive
label	String	Sentiment category — one of `positive`, `negative`, or `neutral`

📊 Dataset Summary

Property	Value
Total Rows	2,526,098
Columns	6
File Formats	JSON / Parquet / Pandas / Polars / Croissant
License	MIT
Author	Md Abdullah Al Mamun
Year	2025
Source	Refined version of Tweets Sentiment Dataset

💡 Usage Example (Python)

python

from datasets import load_dataset
# Load dataset from Hugging Face
ds = load_dataset("bdstar/Tweets-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:

bibtex

@dataset{bdstar2025Tweets,
  title        = {Tweets-Sentiment-Analysis},
  author       = {Md Abdullah Al Mamun},
  year         = {2025},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/datasets/bdstar/Tweets-Sentiment-Analysis}
}

📬 Contact

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