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--- |
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license: cc0-1.0 |
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language: |
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- en |
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- fr |
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- tr |
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tags: |
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- art |
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- emoji |
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- brainrot |
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pretty_name: Youtube shorts comments dataset |
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size_categories: |
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- 100K<n<1M |
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--- |
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# YouTube Shorts Comments Dataset |
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Acollection of **1.4M+ comments** scraped from YouTube Shorts - a mix of brainrot, emoji spam, and English, Turkish, French, and unidentifiable langauges. Perfect for NLP experiments, meme research, or just laughing at internet absurdity. |
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- Inspired from htmx (Idk how to call him lmao) |
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- [Fine-tuned distilgpt2 on this dataset](https://huggingface.co/PingVortex/Youtube-shorts-comment-generator) |
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## Facts 🤔 |
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- I calculated that average amount of emojis in a YouTube short comment is 4.59 |
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- Sources: This dataset, Python script |
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## Dataset Details 🔍 |
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- **Rows**: 1,475,500 comments |
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- **Original Size**: 105 MB (raw) |
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- **Parquet Size**: 41 MB (optimized) |
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- **Sample Content**: Emoji storms, random links, broken English etc. |
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## Fields 📋 |
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Single column: |
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`text` (string) - The raw comment text with emojis/links |
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## Suggested Uses 🤖 |
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- Emoji frequency analysis |
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- Short-form text classification |
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- Internet culture studies |
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- Training a "cursed" language model |
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- Measuring how many 😂 fit in one comment |
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## License 📜 |
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**CC0 1.0 Universal** (Public Domain) |
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*Do whatever you want - no attribution required* |
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## Shoutouts 🔊 |
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- Subscribe to [FaceDev](https://youtube.com/@FaceDevStuff) (not sponsored, just cool) |
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- Join my Discord Server: [PingVortex Discord](https://discord.gg/At3CcCqcR2) |
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*"12 millions view 😂😂😂 good god 🤦♂️" - Anonymous YouTube Shorts Kid* |
