Youtube Video Optimizer Using Ml
Year: 2025
Type: article
Abstract: ABSTRACTIn today's fast-paced world, people have limited time to consume content. This has led to the increasingpopularity of video summarization, which aims to provide users with a condensed and more digestibleversion of a longer video. In this project, we will develop a YouTube summarization system usingNatural Language Processing (NLP) techniques. The system will take a video as input and output ashorter, summarized version of the video. The summarized version will contain the most importantand relevant information from the original video, making it easier for users to understand andremember. To achieve this, we will use a combination of NLP techniques, such as named entityrecognition, part-of-speech tagging, and dependency parsing, to extract the key concepts andrelationships from the video's closed captions. To evaluate the performance of the system, we will usea dataset of YouTube videos and their corresponding summaries. We will train the model on thisdataset and test its accuracy and effectiveness on new, unseen videos. Keywords: You Tube Video Downloading, Video Transcribing, Video Filtering, JavaScript, Python,MySQL.
Authors: Pranit Adhangle, Yogesh Kamankar, Darshan Aher, Aaryan Ahirrao, Prof. Mahesh P. Bhandakkar
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930461886
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