Analysis on Natural Language Processing Using Page Ranking Algorithm on YouTube Videos
Jefferson A. Costales, Janice A. Abellana, Joel Gracia, Madhavi Devaraj · 2021
In today's connected society, YouTube represents a platform where digital content is curated based on the preference of its everyday users. Relevance of search results have long been a debate among the platform providers, content creators, viewers and regulators. In this study, we proposed an approach that employs natural language processing technology and sentiment analysis to improve the accuracy of the page ranking algorithm in YouTube. To test the applicability of our proposal, we tested our algorithm to an existing data set and discuss the results and its relevance. We present our literature review, our methodology, the analysis of the results and highlight the implications of our study.