Comment Volume Prediction using Regression

@article{Kaur2016CommentVP,
  title={Comment Volume Prediction using Regression},
  author={Mandeep Kaur and Prince Verma},
  journal={International Journal of Computer Applications},
  year={2016},
  volume={151},
  pages={1-9}
}
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… 
1 Citations

Figures and Tables from this paper

Factorization Machines for Blog Feedback Prediction

TLDR
It is concluded that factorization machines are competitive with multilayer perceptron networks, linear regression and RBF network and how parameters (feature weights and interaction weights) of factorization machine are learned is analyzed.

References

SHOWING 1-10 OF 26 REFERENCES

Feedback Prediction for Blogs

TLDR
This work presents a proof-of-concept industrial application, developed in cooperation with Capgemini Magyarorszag Kft, that allows to predict the number of feedbacks that a blog document is expected to receive.

News Comments: Exploring, Modeling, and Online Prediction

TLDR
The log-normal and the negative binomial distributions for modeling comments from various news agents are compared, and the feasibility of online prediction of the number of comments, based on the volume observed shortly after publication, is examined.

Dish comment summarization based on bilateral topic analysis

TLDR
A novel approach to tackle the problem of restaurant comment summarization, with a core technique on the new bilateral topic analysis model on the commentary text data, which is effective to generate high-quality summary using representative snippets from the text comments.

Digging Digg: Comment Mining, Popularity Prediction, and Social Network Analysis

TLDR
Using comment information available from Digg, a co-participation network between users is defined, and an entropy measure is inferred to infer that users at Digg are not highly focused and participate across a wide range of topics.

Predicting the volume of comments on online news stories

On-line news agents provide commenting facilities for readers to express their views with regard to news stories. The number of user supplied comments on a news article may be indicative of its

Pace Regression

This paper articulates a new method of linear regression, “pace regression,” that addresses many drawbacks of standard regression reported in the literature—particularly the subset selection problem.

An analysis of Facebook's graph search

TLDR
An analysis of graph search on Facebook is presented, together with possible negative consequences, and guidance as to best practices to follow in order to minimise the cyber security threats imposed by Facebook's graph search.

An Analysis of Reduced Error Pruning

TLDR
This paper clarifies the different variants of the Reduced Error Pruning algorithm, brings new insight to its algorithmic properties, analyses the algorithm with less imposed assumptions than before, and includes the previously overlooked empty subtrees to the analysis.

Predicting User-to-content Links in Flickr Groups

  • Sumit NegiS. Chaudhury
  • Computer Science
    2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
  • 2012
TLDR
The proposed method for predicting user-to-content links takes into account both community effect and content effect and results on real-world Flickr Group data reveals that the proposed method shows good performance for the user- to-content link prediction task.

Predicting the Future with Social Media

  • S. AsurB. Huberman
  • Computer Science
    2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
  • 2010