THE EMOTIONAL EVALUATION OF YOUNG CHILDREN’S VIDEO CONTENTON YOUTUBE
Alt Text: Sentiment Analysis-Based Ranking System for Educational YouTube Videos
Title: The Emotional Evaluation of Young Children’s Video Content on YouTube
Caption: Machine learning-based analysis to rank and optimize educational video recommendations
Description: This paper explores a machine learning-based sentiment analysis system designed to rank YouTube educational content using key parameters like user interactions, comment sentiment, likes, and views.
Keywords: Sentiment Analysis, Logistic Regression, Machine Learning, Flask Web Application, Educational Video Ranking
International Journal of Engineering and Techniques – Volume 11 Issue 3, May 2024
Madda Saivani Reddy, Kallem Bharath Simhareddy, Achana Bhanu Chander, Kallem Deekshith Reddy, Mr. Shek Shakeel
1-4UG Scholars, CSIT, Sri Indu College of Engineering & Technology (A)
5Assistant Professor, CSIT, Sri Indu College of Engineering & Technology (A)
Email: ***
Abstract
YouTube, as a leading social media platform, hosts vast amounts of video content, making search optimization crucial for educational video recommendations. This study proposes a machine learning model that ranks videos based on sentiment analysis of user comments, likes, views, and engagement metrics. Utilizing Logistic Regression and Flask Web Application, the system filters high-quality educational videos to enhance accessibility and reduce search time for students. By analyzing sentiments and interactions, the model ensures relevant content surfaces more effectively.
Keywords
Sentiment Analysis, Logistic Regression, Machine Learning, Flask Web Application, Educational Video Ranking
How to Cite
Madda Saivani Reddy, Kallem Bharath Simhareddy, Achana Bhanu Chander, Kallem Deekshith Reddy, Mr. Shek Shakeel, “The Emotional Evaluation of Young Children’s Video Content on YouTube,” International Journal of Engineering and Techniques, Volume 11, Issue 3, 2024. ISSN 2395-1303.
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