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--- |
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license: cc |
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tags: |
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- text |
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- summarization |
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task_categories: |
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- text-classification |
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- feature-extraction |
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- token-classification |
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- zero-shot-classification |
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- sentence-similarity |
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- text-to-speech |
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size_categories: |
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- 10M<n<100M |
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--- |
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# <span style="color:Red">A larger version of YT-100K dataset -> YT-30M dataset with 30 million YouTube multilingual multicategory comments is also available which can be obtained by directly emailing the author of this dataset.</span> |
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# Introduction |
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This work introduces two large-scale multilingual comment datasets, YT-30M (and YT-100K) from YouTube. The code and both the datasets: YT-30M (full) and YT-100K (randomly selected 100K sample from YT-30M) are publicly released for further research. YT-30M (YT-100K) contains 32M (100K) comments posted by YouTube channel belonging to YouTube categories. Each comment is associated with a video ID, comment ID, commenter name, commenter channel ID, comment text, upvotes, original channel ID and category of the YouTube channel (e.g., News & Politics, Science & Technology, etc.). |
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# Data Description |
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Each entry in the dataset is related to one comment for a specific YouTube video in the related category with the following columns: videoID, commentID, commenterName, commenterChannelID, comment, votes, originalChannelID, category. Each field is explained below: |
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``` |
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videoID: represents the video ID in YouTube. |
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commentID: represents the comment ID. |
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commenterName: represents the name of the commenter. |
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commenterChannelID: represents the ID of the commenter. |
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comment: represents the comment text. |
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votes: represents the upvotes received by that comment. |
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originalChannelID: represents the original channel ID who posted the video. |
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category: represents the category of the YouTube video. |
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``` |
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# Data Anonymization |
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The data is anonymized by removing all Personally Identifiable Information (PII). |
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# Data sample |
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``` |
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{ |
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"videoID": "ab9fe84e2b2406efba4c23385ef9312a", |
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"commentID": "488b24557cf81ed56e75bab6cbf76fa9", |
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"commenterName": "b654822a96eae771cbac945e49e43cbd", |
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"commenterChannelID": "2f1364f249626b3ca514966e3ef3aead", |
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"comment": "ich fand den Handelwecker am besten", |
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"votes": 2, |
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"originalChannelID": "oc_2f1364f249626b3ca514966e3ef3aead", |
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"category": "entertainment" |
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} |
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``` |
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# Multilingual data |
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| **Language** | **Text** | |
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|--------------|---------------------------------------------------| |
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| English | You girls are so awesome!! | |
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| Russian | Точно так же Я стрелец | |
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| Hindi | आज भी भाई कʏ आवाज में वही पुरानी बात है.... | |
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| Chinese | 無論如何,你已經是台灣YT訂閱數之首 | |
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| Bengali | খুিন হািসনােক ভারেতর àধানমন্... | |
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| Spanish | jajajaj esto tiene que ser una brom | |
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| Portuguese | nossa senhora!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!... | |
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| Malayalam | നമസ്കാരം | |
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| Telegu | నమసాక్రం | |
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| Japanese | こんにちは | |
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# License |
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[CC] (https://choosealicense.com/licenses/cc-by-4.0/#) |
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# Bibtex |
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``` |
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@misc{dutta2024yt30mmultilingualmulticategorydataset, |
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title={YT-30M: A multi-lingual multi-category dataset of YouTube comments}, |
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author={Hridoy Sankar Dutta}, |
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year={2024}, |
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eprint={2412.03465}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.SI}, |
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url={https://arxiv.org/abs/2412.03465}, |
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} |
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``` |
