Machine Learnability Analysis of Textclassifications in a Social Bookmarking Folksonomy
J. Illig. University of Kassel, Kassel, Bachelor Thesis, (2008)
Abstract
Social-bookmarking systems such as del.icio.us let users label resources with freely chosen key- words that they find most useful for their intentions. These keywords are also known as tags. The cumulated information from tag assignments by a large community of users constitutes a folksonomy. This thesis analyses how well tag assignments in a folksonomy, that are assigned to semistructured English texts can be found by automatic text classification systems. Therefore, the machine learning algorithms SVM, k-NN, multinomial naive Bayes, and Rocchio have been evaluated for their effectiveness with such data.
%0 Thesis
%1 dipl1
%A Illig, Jens
%C Kassel
%D 2008
%K 1 arbeit bookmarking diplom diplomarbeit folksonomy jens learning machine myown social
%T Machine Learnability Analysis of Textclassifications in a Social Bookmarking Folksonomy
%X Social-bookmarking systems such as del.icio.us let users label resources with freely chosen key- words that they find most useful for their intentions. These keywords are also known as tags. The cumulated information from tag assignments by a large community of users constitutes a folksonomy. This thesis analyses how well tag assignments in a folksonomy, that are assigned to semistructured English texts can be found by automatic text classification systems. Therefore, the machine learning algorithms SVM, k-NN, multinomial naive Bayes, and Rocchio have been evaluated for their effectiveness with such data.
@mastersthesis{dipl1,
abstract = {Social-bookmarking systems such as del.icio.us let users label resources with freely chosen key- words that they find most useful for their intentions. These keywords are also known as tags. The cumulated information from tag assignments by a large community of users constitutes a folksonomy. This thesis analyses how well tag assignments in a folksonomy, that are assigned to semistructured English texts can be found by automatic text classification systems. Therefore, the machine learning algorithms SVM, k-NN, multinomial naive Bayes, and Rocchio have been evaluated for their effectiveness with such data.},
added-at = {2015-10-16T11:49:13.000+0200},
address = {Kassel},
author = {Illig, Jens},
biburl = {https://www.bibsonomy.org/bibtex/29a65067da65e8301182b33b4ae292141/kde-alumni},
interhash = {65c16443f45ffd46175f68d14b4f809a},
intrahash = {9a65067da65e8301182b33b4ae292141},
keywords = {1 arbeit bookmarking diplom diplomarbeit folksonomy jens learning machine myown social},
school = {University of Kassel},
timestamp = {2016-11-29T17:44:45.000+0100},
title = {Machine Learnability Analysis of Textclassifications in a Social Bookmarking Folksonomy},
type = {Bachelor Thesis},
year = 2008
}