An Effective Classification Method for Facebook Data

Fatih Ertam

doi:10.18100/ijamec.2018SpecialIssue30463

Research ArticlesverifiedPeer-ReviewedOpen Access

Pages: 9-13

An Effective Classification Method for Facebook Data

Fatih Ertam

Publication DateSeptember 24, 2017

Volume / IssueVol. , No. (pp. 9-13)

DOI Identifier10.18100/ijamec.2018SpecialIssue30463

subject

Abstract

Today, the use of the internet has become very common. One of the most important reasons for its widespread use is social media tools. Especially Facebook has a very important place in social media tools. For this study, classification was done by using Facebook data. Classifications made by artificial learning algorithms on a previously used data set are compared with accuracy values and learning times. For this purpose, support vector machines (SVM), extreme learning machines (ELM) and K nearest neighbor (kNN) approaches are compared. For the study, SVM and ELM algorithms were observed using different activation functions. For the study with KNN, different K values were tested with different distance metric calculation methods. In the classification approach with ELM, it was observed that higher accuracy values were reached in a shorter time. In addition, Receiver Operating Characteristic (ROC) curves are plotted for the classification in which the best values are obtained for each algorithm.

Keywords:Extreme learning machinemachine learningroc curvessocial media classificationsupport vector machine

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An Effective Classification Method for Facebook Data

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