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Comment Volume Prediction using Regression

by Mandeep Kaur, Prince Verma
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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
10.5120/ijca2016910131

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 = { https://ijcaonline.org/archives/volume151/number1/26194-2016910131/ },
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
Abstract

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.

References
  1. S. M. Tan, P.N, V. Kumar, Introduction to data mining, Pearson Addison Wesley Boston, 2006.
  2. Buza Krisztian, “Feedback Prediction for Blogs”, Springer International Publishing on Data Analysis, Machine Learning and Knowledge Discovery,2014, pp. 145-152. doi:10.1007/978-3-319-01595-8 16.
  3. M.Tsagkias, W. Weerkamp, M. de Rijke, “Predicting the Volume of Comments on Online News Stories”,CIKM’09 Proceedings of the 18th ACM conference on Information and knowledge management , pp.1765-1768, 2009.
  4. Jamali, S. and Rangwala, H., “Digging Digg: Comment Mining, Popularity Prediction, and Social Network Analysis”, Web Information Systems and Mining, IEEE International Conference,2009, pp. 32-38. doi: 10.1109/WISM.2009.15.
  5. M. Tsagkias, W. Weerkamp, M. de Rijke, “News Comments: Exploring, Modeling, and Online Prediction", ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval, Springer, pp.191-203,2010.
  6. Yano, Tae, and Noah A. Smith. “What's Worthy ofComment? Content and Comment Volume in Political Blogs” In 4th International AAAI Conference on Weblogs and Social Media, 2010.
  7. Balali, A. and Rajabi, A. and Ghassemi, S. andAsadpour, M. and Faili, H., “Content diffusion prediction in social networks”, Information and Knowledge Technology (IKT), 5th IEEE Conference,2013, pp. 467-471. doi: 10.1109/IKT.2013.6620114.
  8. Negi, S. and Chaudhury, S., “Predicting User-to-content Links in Flickr Groups”, Advances in Social Networks Analysis and Mining (ASONAM), IEEE/ACM International Conference, 2012 pp. 124-131. doi: 10.1109/ASONAM.2012.31.
  9. Rahman, M.M. , “Intellectual knowledge extraction from online social data”, Informatics, Electronics Vision (ICIEV), IEEE International Conference, 2012, pp. 205-210. doi:10.1109/ICIEV.2012.6317392
  10. Rong Zhang, Zhenjie Zhang, Xiaofeng He, Aoying Zhou, “Dish Comment Summarization Based on Bilateral Topic Analysis” Data Engineering (ICDE), 31st IEEE International Conference, 2015, pp. 483 – 494. doi: 10.1109/ICDE.2015.7113308
  11. S. M. Tan, P.N, V. Kumar, Introduction to data mining, Pearson Addison Wesley Boston, 2006.
  12. Z.C. Khan , Thulani Mashiane, “An analysis of Facebook's Graph Search”, Information Security for South Africa(ISSA), IEEE, pp.1-8, 2014.
  13. Laura V. Galvis Carreno, Kristina Winbladh “Analysis of User Comments: An Approach for Software Requirements Evolution”,35th International Conference on software Engineering ,USA, IEEE, 2013, pp. 582-591. doi: 10.1109/ICSE.2013.6606604.
  14. Yu-Hsiu, Hsueh-Yi Lai “Effects of Facebook Like and Conflicting Aggregate Rating and Customer Comment on Purchase Intentions”, Springer International Publishing in Universal Access in Human-Computer Interaction. Access to Today's Technologies, 2015,pp.193-200.doi: 10.1007/978-3-319-20678-3_19.
  15. Paul W. Ballantine , Yongjia Lin, Ekant Veer, “The influence of user comments on perceptions of Facebook relationship status updates”, Computers in Human Behavior, Elsevier 2015, Volume- 49, pp.50–55. doi:10.1016/j.chb.2015.02.055
  16. N. Leelathakul, K. Chaipah, “Quantitative Effects of using Facebook as a Learning Tool on Students’ Performance” 10th International Joint Conference on Computer Science and Software Engineering (JCSSE),IEEE 2013, pp.87 – 92. doi: 10.1109/JCSSE.2013.6567325.
  17. Sitaram Asur, Bernardo A. Huberman, “Predicting the Future With Social Media” Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference, pp. 492 – 499. doi: 10.1109/WI-IAT.2010.63
  18. https://www.facebook.com/business/success
  19. http://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/
  20. Jan H. Kietzmann , K.Hermkens, Ian P. McCarthy, B. S. Silvestre, “Social media? Get serious! Understanding he functional building blocks of social media” Business Horizons, Elsevier 2011, Volume- 54, Issue 3, pp. 241-251. doi:10.1016/j.bushor.2011.01.005S
  21. Wang Y, Witten IH (1999) Pace regression. Technical Report 99/12, Department of Computer Science, The University of Waikato
  22. Tapio Elomaa, Matti Kaariainen, “An Analysis of Reduced Error Pruning”, Department Journal of Artificial Intelligence Research 15 (2001) 163-187.
Index Terms

Computer Science
Information Sciences

Keywords

Social media Comment volume Pace regression REP Tree.

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