Multi-Perspective Video Game Analysis on YouTube: A Hybrid Time Series-Based Bee Colony Optimization Approach (TSBBCOA)
Keywords:
ABC (Artificial Bee Optimization), Time Series-Based Bee Colony Optimization Algorithm (TSBBCOA), Video Marketing, YouTube, Time Series, Optimal Solution.Abstract
Within the domain of digital media, YouTube functions as a flourishing centre for a wide range of content, encompassing evaluations, analyses, and demonstrations of video games. However, the existing constraints inherent in contemporary approaches for analysing video game material on the YouTube platform highlight an urgent need for a novel and innovative methodology. Introducing the Time Series-Based Bee Colony Optimization Algorithm (TSBBCOA), an innovative approach developed to tackle the limitations above effectively. The current systems encounter significant limitations, including difficulty capturing the wide range of perspectives and opinions expressed in YouTube videos, challenges in scaling up to accommodate the platform's rapid content expansion, and inability to handle temporal changes or process large amounts of data effectively. The constraints above highlight the need for an algorithmic approach such as TSBBCOA. This methodology leverages time series analysis and bee colony optimization to offer a comprehensive and precise understanding of the multifaceted landscape of video games on the YouTube platform. This research presents a conceptual framework of TSBBCOA, explores its mathematical modelling and algorithmic complexities, and illustrates its actual implementation, highlighting its effectiveness in addressing the limitations of existing systems. The TSBBCOA framework is a very effective solution urgently required to enable a thorough and intelligent video game content analysis from several perspectives in the current digital media era.
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