YouTube Career Analysis with the Combination of Trending and Sentiments Analysis

Abstract

Khin Than Nyunt and Naw Thiri Wai Khin

The advances in technology users frequently utilize online platforms and direct applications to access YouTube. The amount of people who make a career from YouTube has also increased dramatically along with the growth in the number of people who use the platform nowadays. They are having trouble selecting a YouTube career since it is extremely hard to determine which of the many categories and channels is most popular and appropriate for a particular country. The user can choose a successful career path and potentially become a successful YouTuber if they are aware of the most popular category or channel. Consequently, it has been suggested that YouTube Trending Analysis can assist those who are struggling to decide on a professional life, especially those who want to use YouTube as a source of income. The most popular careers in the United States (U.S.), Japan, and India right now are determined based on the content criteria of each video in YouTube. Additionally, the proposed machine learning model compares with Naïve Bayes Classifier, a method-based approach, to do sentiment analysis. Linear regression model is used to get the predicted results from trending analysis, then user is given an accurate, robust, and genuine recommendation for the proposed model's outcome in the visualization.

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