A Framework to Predict the Quality of a Video for Popularity on Social Media - Abqa Javed; Nimra Abid; Muhammad Shoaib; Muhammad Farrukh Shahzad; Fahad Sabah; Raheem Sarwar (Engineering Reports, 2025)
ABSTRACT YouTube has become a dominant force in digital media, yet current video popularity analytics remain limited in capturing the emotional and cultural dimensions of viewer engagement, particularly in underrepresented regions like Pakistan. While existing research focuses predominantly on Western markets and quantitative metrics (views, likes, comments), these approaches overlook sentiment‐driven interactions critical to understanding regional audience behavior. This study bridges this gap by introducing a sentiment‐aware framework for YouTube video classification in Pakistan, combining traditional popularity metrics with advanced sentiment analysis of user comments. We curated the PAK VIDEOS (2021–2023) dataset using YouTube Data APIs, comprising metadata and user comments from Pakistan's top trending videos. Leveraging Natural Language Processing (NLP) techniques, we extracted sentiment scores from comments to classify videos into four categories: non‐popular, overwhelmingly positive, overwhelmingly negative, and neutral. This hybrid approach enabled a nuanced evaluation of content reception beyond quantitative metrics. Four machine learning models—random forest, stochastic gradient descent classifier (SGDC), gradient boosting, and XGBoost—were evaluated for classification. XGBoost achieved superior performance (84.3% accuracy), outperforming baseline models by up to 20%. Our framework demonstrates that integrating sentiment analysis significantly enhances popularity prediction, particularly in culturally distinct contexts.