The Enhancement of Trending Analysis for YouTube Social Media Platform
Year: 2024
Type: article
Abstract: Developments in technology have led to a rise in YouTube users, but choosing the right career can be challenging due to the numerous categories and channels available. YouTube trend analysis can help users make informed decisions about their professional lives, especially those seeking to use YouTube as a source of income. A hybrid system combining trending and sentiment analysis using the linear regression method of prediction for trending and the multinomial Naive Bayes classification model for sentiment analysis is proposed for the country United States (US), 2023. This system provides recommendations for YouTube career choices and identifies the most trending categories and channels, providing valuable insights for those struggling to make a living on the platform.
Author: Khin Than Nyunt
Institution: Naypyitaw State Polytechnic University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405380695