---
license: mit
task_categories:
- text-classification
- token-classification
language:
- en
tags:
- twitter
- tweets
- sentiment
- social
- multi-class
pretty_name: Tweets-Sentiment-Analysis
size_categories:
- 10M<n<100M
---

# 🐦 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-tweet_sentiment_extraction         | 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](https://huggingface.co/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](https://techntuts.com/)
💼 **Linkedin:** [WebRock](https://www.linkedin.com/in/webrock/)