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Prognosticate Trending Days of Youtube Videos Tags Using K-Nearest Neighbor Algorithm | IEEE Conference Publication | IEEE Xplore

Prognosticate Trending Days of Youtube Videos Tags Using K-Nearest Neighbor Algorithm

Publisher: IEEE

Abstract:

YouTube is a video-sharing website where users may publish, watch, share, and comment on videos and other media. The proliferation of technological gadgets, combined with...View more

Abstract:

YouTube is a video-sharing website where users may publish, watch, share, and comment on videos and other media. The proliferation of technological gadgets, combined with rapid advancements in technology, has resulted in an increase in trending videos on the platform, where videos and content receive hundreds of thousands, if not millions, of views within minutes of being uploaded and continue to trend throughout the day. This study uses the US YouTube Trending dataset, which includes 130591 occurrences and was acquired from the kaggle repository between August 11, 2020 to May 14, 2022. This study used qualitative and quantitative methods to analyze the YouTube videos dataset, and then performed a predictive analysis on the trending video tags, predicting how a particular video on YouTube might trend in the next two to eight days by predicting the trending of such videos for the next two to eight days and showing their accuracy results using the K-nearest neighbor algorithm (KNN). The model that was utilized to perform the prediction analysis has an accuracy of around 98 percent.
Date of Conference: 01-03 November 2022
Date Added to IEEE Xplore: 02 March 2023
ISBN Information:
Publisher: IEEE
Conference Location: Abuja, Nigeria

I. Introduction

With millions of daily visitors and a substantial influence on consumer behavior, attitudes, and opinions, Youtube is the most popular video site on the planet. As a result, video optimization[2] has monetary value, and many businesses [3]use it to gain traction and build interest in their products and services. Because of widespread Internet access and the advent of [4]Web 2.0 services, a vast and ever-increasing amount of online data has been introduced into the digital world. Non-traditional channels have allowed content makers to reach consumers in previously inconceivable numbers. [5]Among the many sorts of content available on the internet, online videos are currently the most popular. Video traffic accounted for around 64% of all Internet traffic in 2014, and this figure is anticipated to climb to 80% by 2019 [1]. K-nearest neighbors could be a simple algorithmic software that maintains all existing instances and categorizes new cases based on their similarity. The algorithmic program k-nearest neighbors is a non-parametric technique for classification and regression (k-NN). In each situation, the input is taken from the k-nearest coaching examples within the feature area. The outcome will alter depending on whether k-NN is used for classification or regression: The output of k-NN classification might be a category membership. A majority of its neighbors judge an object [6], and it is assigned to the category that is most common among its k closest neighbors (k could be a positive whole number, usually small). If k = 1, the object is exclusively assigned to that one category of closest neighbors. A k-NN regression is used to determine the item's property price. This is the average of the costs of k of your closest neighbors. K-NN[7] is a type of example-based learning, also referred to as lazy learning, in which work is only approximated regionally and all calculations are postponed until classification. The k-NN algorithmic program is one of the only ones among machine learning algorithms. Applying weight to the contributions of the neighbors[8], so that the closest neighbors contribute more to the common than the farther away neighbors, is an effective method for both classification and regression.

References

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