Leveraging spark-based machine learning algorithm for audience sentiment analysis in youtube content

Abstract

In today’s digital era, the generation and sharing of information are rapidly expanding. The increased volume of complex data is big data. YouTube is the primary source of big data. The proliferation of the internet and smart devices has led to a significant increase in content creators on social media platforms, with YouTube being a prominent example. There has been a substantial increase in content creators across various social media platforms, with YouTube emerging as one of the foremost platforms for content generation and sharing. YouTubers face challenges in enhancing content strategies due to the growing number of comments, such as big data on shared videos. Reading and finding viewers’ opinions of such a large amount of data through manual methods is time-consuming and challenging and makes it hard to understand people’s sentiments. To address this, spark-based machine learning algorithms have emerged as a transformative tool for content creators to understand the audience. The Improved Novel Ensemble Method (INEM) algorithm is designed to predict viewers’ sentiments and emotional responses based on the content they interact through the comments. The proposed results provide valuable insights for content creators, helping them refine the strategies to optimize the channel’s revenue and performance. Fit Tuber Channel is analyzed to perform the sentiment of user comments.