Paper Info
Title
Text classification in the Turkish marketing domain for context sensitive ad distribution
Abstract
In this paper, we construct and compare several feature extraction approaches in order to find a better solution for classification of Turkish Web documents in the marketing domain. We produce our feature extraction techniques using characteristics of the Turkish language, structures of Web documents and online content in the marketing domain. We form datasets in different feature spaces and we apply several support vector machine (SVM) configurations on these datasets. We conduct our study considering the performance needs of practical context sensitive systems. Our results show that linear kernel classifiers achieve the best performance in terms of accuracy and speed on text documents expressed as keyword root features.
Year
DOI
Venue
2009
10.1109/ISCIS.2009.5291861
ISCIS
Keywords
Field
DocType
information retrieval,context sensitive ad distribution,world wide web,linear kernel classifiers,artificial intelligence,learning (artificial intelligence),text classification,data mining,marketing data processing,machine learning,support vector machine,internet,feature extraction techniques,turkish marketing domain,turkish web documents,turkish language,natural language processing,text analysis,document handling,support vector machines,classification algorithms,learning artificial intelligence,artificial intelligent,accuracy,feature space,feature extraction,principal component analysis,kernel
Kernel (linear algebra),Turkish,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Document handling,Statistical classification,Marketing,Principal component analysis,Machine learning,The Internet
Conference
ISBN
Citations 
PageRank 
978-1-4244-5023-7
0
0.34
References 
Authors
5
2
Name
Order
Citations
PageRank
Melih Engin100.34
Tolga Can226816.39