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Reseach Article

Comment Volume Prediction using Regression

by Mandeep Kaur, Prince Verma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 151 - Number 1
Year of Publication: 2016
Authors: Mandeep Kaur, Prince Verma

Mandeep Kaur, Prince Verma . Comment Volume Prediction using Regression. International Journal of Computer Applications. 151, 1 ( Oct 2016), 1-9. DOI=10.5120/ijca2016910131

@article{ 10.5120/ijca2016910131,
author = { Mandeep Kaur, Prince Verma },
title = { Comment Volume Prediction using Regression },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2016 },
volume = { 151 },
number = { 1 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016910131 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:55:54.379309+05:30
%A Mandeep Kaur
%A Prince Verma
%T Comment Volume Prediction using Regression
%J International Journal of Computer Applications
%@ 0975-8887
%V 151
%N 1
%P 1-9
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

The latest decade lead to a unconstrained advancement of the importance of online networking. Due to the gigantic measures of records appearing in web organizing, there is a colossal necessity for the programmed examination of such records. Online networking customer's comments expect a basic part in building or changing the one's acknowledgments concerning some specific indicate or making it standard. This paper demonstrates a preliminary work to exhibit the sufficiency of machine learning prescient calculations on the remarks of most well known long range informal communication site, Facebook. We showed the customer remark patters, over the posts on Facebook Pages and expected that what number of remarks a post is depended upon to get in next H hrs. To automate the technique, we developed an item display containing the crawler, information processor and data disclosure module. For prediction, we used the Linear Regression model (Simple Linear model, Linear relapse model and Pace relapse model) and Non-Linear Regression model(Decision tree, MLP) on different data set varieties and evaluated them under the appraisal estimations Hits@10, AUC@10, Processing Time and Mean Absolute Error.

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Index Terms

Computer Science
Information Sciences


Social media Comment volume Pace regression REP Tree.