| --- |
| 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/) |
