YouTube (the world-famous video sharing website) maintains a list of the top trending videos on the platform. According to Variety magazine, “To determine the year’s top-trending videos, YouTube uses a combination of factors including measuring users interactions (number of views, shares, comments and likes). Note that they’re not the most-viewed videos overall for the calendar year”. Top performers on the YouTube trending list are music videos (such as the famously virile “Gangam Style”), celebrity and/or reality TV performances, and the random dude-with-a-camera viral videos that YouTube is well-known for.
This dataset is a daily record of the top trending YouTube videos in US.
Data includes the video title, channel title, publish time, tags, views, likes and dislikes, description, and comment count.
This dataset was submitted by "Mitchell J" on kaggle. The data was retrieved through Youtube API.
Did an Exploratory Data Analysis on the data to derive few meaningful trends through visualizations.
Sentiment analysis on the description to see if it effects views,likes..etc. Analysis of what factors will help a newly uploaded video to trend. Analyzing how to convert views into likes and comments. (All these without taking in the actual content of the video)
link to data: https://www.kaggle.com/datasnaek/youtube-new/home Feel free to fork and develope upon the code!