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--- |
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license: mit |
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task_categories: |
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- text-classification |
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- token-classification |
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language: |
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- en |
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tags: |
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- twitter |
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- tweets |
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- sentiment |
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- social |
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- multi-class |
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pretty_name: Tweets-Sentiment-Analysis |
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size_categories: |
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- 10M<n<100M |
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--- |
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# 🐦 Tweets-Sentiment-Analysis (bdstar/Tweets-Sentiment-Analysis) |
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## 🧠 Overview |
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A **refined and merged version of Tweets text sentiment datasets**, providing a clean and well-balanced dataset for **sentiment classification** across three sentiment categories: |
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**`positive`**, **`negative`**, and **`neutral`**. |
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This dataset is split into three parts — **train**, **test**, and **validation** — each sourced from highly reputable open datasets. |
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It is designed for training, evaluating, and benchmarking **NLP models** for **Tweets Sentiment Analysis** and other **social media text classification** tasks. |
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--- |
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## 🗂️ Dataset Splits |
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| # | Split | Name | Negative | Neutral | Positive | % Negative | % Neutral | % Positive | Total | |
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|---|-------------|-----------------------------------------|----------|---------|----------|------------|-----------|------------|----------| |
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| 1 | Train | Sentiment140 (positive-sentence) | 71,462 | 233,345 | 483,261 | 9.067999 | 29.609754 | 61.322246 | 788,068 | |
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| 2 | Train | Sentiment140 (negative-sentence) | 451,341 | 191,650 | 136,801 | 57.879665 | 24.577067 | 17.543268 | 779,792 | |
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| 3 | Train | DailyDialog | 12,623 | 45,674 | 20,226 | 16.075545 | 58.166397 | 25.758058 | 78,523 | |
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| 4 | Test | ChatGPT Tweets Sentiment Analysis | 194,425 | 360,060 | 295,108 | 22.884487 | 42.380293 | 34.735220 | 849,593 | |
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| 5 | Validation | mteb-tweet_sentiment_extraction | 10,083 | 7,969 | 12,070 | 33.473873 | 26.455747 | 40.070380 | 30,122 | |
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| | **Total** | — | **739,934** | **838,698** | **947,466** | **29.291579** | **33.201325** | **37.507096** | **2,526,098** | |
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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) |
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--- |
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## 🧩 Column Descriptions |
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| Column | Type | Description | |
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|---------|------|-------------| |
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| **ID** | Integer | Auto-incremental unique ID for each row | |
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| **text** | String | Tweet text content | |
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| **negative** | Float | Possiblity the text be a negative | |
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| **neutral** | Float | Possiblity the text be a neutral | |
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| **positive** | Float | Possiblity the text be a positive | |
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| **label** | String | Sentiment category — one of `positive`, `negative`, or `neutral` | |
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--- |
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## 📊 Dataset Summary |
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| Property | Value | |
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|-----------|-------| |
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| **Total Rows** | 2,526,098 | |
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| **Columns** | 6 | |
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| **File Formats** | JSON / Parquet / Pandas / Polars / Croissant | |
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| **License** | MIT | |
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| **Author** | Md Abdullah Al Mamun | |
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| **Year** | 2025 | |
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| **Source** | Refined version of Tweets Sentiment Dataset | |
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--- |
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## 💡 Usage Example (Python) |
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```python |
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from datasets import load_dataset |
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# Load dataset from Hugging Face |
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ds = load_dataset("bdstar/Tweets-Sentiment-Analysis") |
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# Access splits |
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train = dataset["train"] |
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test = dataset["test"] |
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validation = dataset["validation"] |
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# Display sample |
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print(train[0]) |
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``` |
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--- |
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## 🏷️ Citation |
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If you use this dataset in your research or application, please cite as: |
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```bibtex |
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@dataset{bdstar2025Tweets, |
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title = {Tweets-Sentiment-Analysis}, |
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author = {Md Abdullah Al Mamun}, |
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year = {2025}, |
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howpublished = {Hugging Face}, |
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url = {https://huggingface.co/datasets/bdstar/Tweets-Sentiment-Analysis} |
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} |
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``` |
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--- |
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## 📬 Contact |
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For questions, improvements, or collaboration: |
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**Author:** Md Abdullah Al Mamun |
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📧 **Email:** mamunbd.ruet@gmail.com |
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🌐 **Website:** [TechNTuts](https://techntuts.com/) |
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💼 **Linkedin:** [WebRock](https://www.linkedin.com/in/webrock/) |
