In this paper, the proposed system jointly combines the properties of YouTube and Twitter. The linear regression method is used to predict the Top-N popular video's. This method is designed to improve the analysis of popular video's and their popularity trends. It is for understanding the video popularity characteristics and predicting the future video popularity. They have direct implications in various contexts such as service design, advertisement planning, network management and so on. The data are collected from YouTube and Twitter API's.
Published In:IJCSN Journal Volume 8, Issue 3
Date of Publication : June 2019
Pages : 254-257
Figures :03
Tables : 01
Christy Kunjumon :
received her B.Tech (CSE) degree from University of Kerala in 2017. She is currently pursuing her Masters in Computer Science & Engineering from KTU.
Dr. Radhakrishnan B :
is working as the Head of CSE department. He has more than 14 years experience in teaching and has published papers on data mining and image processing. His research interests include image processing, data mining, image mining.
popularity prediction, YouTube, social media, Multilayer Perceptron, linear regression.
In this paper, we have shown for a given YouTube videos for a specific period of time. The goal of the project is to use machine learning techniques to predict the popularity of YouTube videos and social feature network. Here the YouTube properties and twitter properties are considered as the input. Based on the properties, to predict the popularity of individual video. This study proposes a new prediction model based on multilayer perceptron and Linear Regression The result shows that the proposed model is better as compared with the previous models. Also, this model gives a consistent and accurate result.
[1] William Hoiles, Student Member, IEEE, Anup Aprem, and Vikram Krishnamurthy, Fellow, IEEE. Engagement dynamics and sensitivity analysis of YouTube videos. [2] Peter Schultes, Verena Dorne and Franz Lehner. Leave a Comment! An In-Depth Analysis of User Comments on YouTube, University of Passau, Passau, Germany schult16@stud.uni-passau.de, {Verena. dorner, Franz. Lehner}@uni-passau.de [3] Chenyu Li (student member, IEEE), Jun Liu (member, IEEE) and Shuxin Ouyang. Characterizing and Predicting the Popularity of Online Videos, Beijing Key Laboratory of Network System Architecture and Convergence, Center for Data Science, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China [4] Zhiyi Tan, Ya Zhang IEEE Predicting the Top-N Popular Videos via a Cross-Domain Hybrid Model. [5] Tomasz Trzci´Nski and Przemyslaw Rokita, IEEE Transactions On Multimedia. predicting popularity of online videos using Support Vector Regression.
