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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.
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