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Dataset with popular YouTuber's video subtitles

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kaggle

Intro

Founded and maintained since 2005, YouTube is one of the internet's biggest platforms. With their number of videos watched per day exceeding 1 Billion, it's easy for any user to differentiate genres just by glancing at the thumbnail and the Title. My inspiration to make this dataset was to try and answer the question of whether it is equally easy for a computer to do.

The Transcript column in the dataset contains the subtitles for the respective videos. However, the reliability of the subtitles may vary. Even though the auto-generated subtitles work great (most of the time). Sometimes under heavy pressure of thick accents, it lets go of the ball. Please consult the CC attribute to check whether the subtitle is auto-generated or not. 1381 of these video subtitles are auto-generated, the rest of the 1134 are manual ones.

Since the values of Subscribers and Views are based on the time when the dataset was generated. That's to be taken into account. The most recent version of this dataset was generated on 05-Feb-2022.

Description

This dataset contains subtitles from over 91 different YouTubers, ranging from all different kinds of categories. The data were collected and cleaned (as much as necessary) by me. Currently, the dataset contains 2515 unique videos and their subtitles. There are 11 columns in the dataset. The purpose of each column is as follows:

No Column Dtype Description
1 Id str Unique ID for the video. (e.g., dQw4w9WgXcQ)
2 Channel str Name of the YouTube channel.
3 Subscribers str How many subscribers did the channel have while collecting the dataset
4 Title str Title of the video
5 CC int Did the video have manual subtitles? (Possible values 0 or 1, where 0 means that the Transcript is auto-generated, and may be less reliable)
6 URL str URL of the video (e.g., https://www.youtube.com/watch?v=dQw4w9WgXcQ)
7 Released str When the video was released
8 Views str How many views did the video have during the collection of this dataset
9 Category str Category of the channel (e.g., Science, Comedy, etc)
10 Transcript str Subtitle for the video
11 Length str Duration of the video

Genres and channels

Genre Channels #
Comedy Brooklyn Nine-Nine, DRIVETRIBE, Incognito Mode, Internet Historian, Joma Tech, Key & Peele, OverSimplified, Parks and Recreation, penguinz0, Screen Junkies, Team Coco, The Graham Norton Show, The Grand Tour, The Office, Top Gear 15
Science 3Blue1Brown, Dr. Becky, ElectroBOOM, Kurzgesagt – In a Nutshell, Lex Clips, Mark Rober, NileRed, PBS Space Time, SEA, SmarterEveryDay, Veritasium, Vsauce 12
Automobile Car Throttle, carwow, ChrisFix, Donut Media, DRIVETRIBE, The Grand Tour, The Stig, TheStraightPipes, Throttle House, Top Gear 10
VideoGames EpicNameBro, gameranx, jacksepticeye, Let's Game It Out, LevelCapGaming, LGR, Markiplier, Shroud, TheWarOwl, videogamedunkey 10
Food About To Eat, Bon Appétit, Eater, Epicurious, First We Feast, FoodTribe, Gordon Ramsay, Hell's Kitchen, Munchies, Mythical Kitchen, The F Word 11
Entertainment Brooklyn Nine-Nine, BuzzFeedVideo, Doctor Who, First We Feast, MrBeast, Parks and Recreation, Screen Junkies, Team Coco, The Graham Norton Show, The Office, The Try Guys 11
Informative Barely Sociable, Captain Disillusion, CNET, Coder Coder, Internet Historian, JCS - Criminal Psychology, LEMMiNO, OverSimplified, Sam O'Nella Academy, The Infographics Show, Vox 11
Blog Abroad in Japan, CDawgVA, DramaAlert, Drew Gooden, Incognito Mode, Joma in NYC, JRE Clips, Lex Clips, MrBeast, penguinz0, The Try Guys 11
News A&E, BBC News, Insider News, NBC News, NowThis News, Sky News, SomeGoodNews, TechLinked, The Daily Show with Trevor Noah, VICE 10
Tech Austin Evans, Coder Coder, Fireship, Hardware Canucks, Joma Tech, Linus Tech Tips, Marques Brownlee, TechLinked, Techquickie, Web Dev Simplified 10

Note

I am open to suggestions please feel free to let me know of any major Categories or channels that I've missed or you'll like to be included. I'll try my best to include them to the dataset.

Thanks!