Update Readme.md

README.md

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----
-license: mit
----
+---
+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/)