Random Articles
Reseach Article
A Predictive Methodology for Analysis of Social Media Influence in Brand Building
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 32 |
| Year of Publication: 2018 |
| Authors: Pallavi Bagde, Dharmendra Mangal |
10.5120/ijca2018918231
|
Pallavi Bagde, Dharmendra Mangal . A Predictive Methodology for Analysis of Social Media Influence in Brand Building. International Journal of Computer Applications. 182, 32 ( Dec 2018), 11-17. DOI=10.5120/ijca2018918231
Abstract
The data mining and their techniques are classically used for analysis of data patterns. These patterns recovery is used to estimate the similar patterns on the newly obtained data. Now a day’s the data mining and their techniques are frequently utilized for various business intelligence applications. The main aim of the proposed work is to recover the social media post popularity and future trends of the popularity patterns. In this context two data mining models are applied on facebook post dataset. The post dataset contains the user activities on the facebook posts published by a page manager. The user activity dataset is first processed using k-means clustering algorithm which is an unsupervised learning technique. That algorithm is applied in order to estimate which kinds of post are highly attracting users and which kind of contents are less. In addition of that for measuring the growth and future trends of a post the C4.5 (J48) decision tree algorithm is applied. By traversing the generated decision tree the post popularity trend is estimated. The implementation of the proposed technique is performed using JAVA technology. Additionally the performance of system is measured in terms of memory and time consumption during data analysis. According to obtained results the proposed technique is effective and able to recover required data patterns from the facebook post dataset.
References
- Altyeb Altaher, Ahmed Hamza Osman, “An Intelligent Approach for Predicting Social Media Impact on Brand Building”, Journal of Theoretical and Applied Information Technology, Vol.95. No.17, 15th September 2017.
- Padma Janani M and Prabhalammbeka B S, “Predicting the Influences of Social Networks on Brand Building”, International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 10, October 2017, pp. 308–317.
- Bernardo Varela Vala, “The impact of Social Media in Brand Building”, MS Dissertation, ISCTE Business School, September 2015
- Shen zihao, Wang Hui, “Research on E-commerce Application Based on Web Mining”, 2010, IEEE.
- Jaideep Srivastava, Prasanna Desikan and Vipin Kumar, “Chapter 21- Web Mining — Concepts, Applications, and ResearchDirections”,
- http://dmr.cs.umn.edu/Papers/P2004_4.pdf
- “Chapter 1: Introduction”, Sodhganga, available online at: http://shodhganga.inflibnet.ac.in/bitstream/10603/11075/5/05_chapter1.pdf
- Web Content Mining: Tutorial given at WWW-2005 and WISE-2005 available online at: https://www.cs.uic.edu/~liub/WebContentMining.html
- Yogeswari Suppiah, Raja Mohd Tariqi Raja and Mohd Fahmi Mohamad Amran, “A Study on Social Data Analytics and Privacy Concern among Social Media Users”, International Journal of Computer Applications (IJCA) Volume 149 – No.5, September 2016.
- “Social Media Analytics: Driving Better Marketing Decisions: Insights into Customer Behavior”, White paper, Cybage.
- Wang, Juntao, and Xiaolong Su. "An improved K-Means clustering algorithm." In Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on, pp. 44-46. IEEE, 2011.
- Mishra, Kundan Kumar, and Rahul Kaul. "Audit Trail Based on Process Mining and Log." International Journal of Recent Development in Engineering and Technology 1, no. 1 (2013).
Index Terms
Keywords