Employ Innovative Algorithms to Assign Weights to YouTube Search Results and Extract Education - Related Data
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Abstract
The rapid advancement of data science, artificial intelligence, and machine learning significantly impacts education, with generative AI technologies like ChatGPT increasingly influencing learners. This study analyzes YouTube video view data to extract educational insights through a weighted approach. We found that videos with over 100,000,000 views received a weight of 30, while those with 10,000 to 99,999 views were assigned a weight of 1. In sentiment analysis using Python, we determined an average sentiment weight of 0.722 for education, indicating a generally positive public perception. Our proposed "Weighted Extraction Weight Addition - TF-IDF" algorithm calculated a TF-IDF value of 0.060 for "education," yielding a final value of 754.16 when weighted with 12,513 views. Furthermore, the "Weighted Extraction Maximum Weight - TF-IDF" algorithm identified "child" as having a maximum weight of 30, highlighting public interest in children's educational content. This analysis aims to enhance educational development by providing valuable information derived from YouTube search results.
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References
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