H. Fromm, T. Wambsganss, and M. Söllner. European Conference on Information Systems (ECIS) 2019, page 12. European Conference on Information Systems (ECIS), (June 2019)
Abstract
Recently, text mining has received special attention from both researchers and practitioners, since it enables the development of intelligent and automated services. Text mining has been influenced by different disciplines like computer science, statistics, computational linguistics and library and infor-mation sciences. However, text mining features that evolved in one particular discipline are often un-known or rarely used in the other disciplines. No scientific feature framework exits which facilitates costly feature engineering and evaluation. Therefore, we aim to develop a novel text mining feature taxonomy, which helps researchers and practitioners to develop, refine, compare and evaluate their text mining studies. In this research in progress paper, we focus on laying the foundation for our tax-onomy development by presenting our first two research cycles. Here, we were aiming for diversity, not completeness. We derived five dimensions and classified different text features accordingly to pro-vide a deeper understanding.
%0 Conference Paper
%1 conf/ecis/FrommWS19
%A Fromm, Hansjörg
%A Wambsganss, Thiemo
%A Söllner, Matthias
%B European Conference on Information Systems (ECIS) 2019
%D 2019
%E vom Brocke, Jan
%E Gregor, Shirley
%E Müller, Oliver
%I European Conference on Information Systems (ECIS)
%K itegpub pub_msö pub_wise-kassel
%P 12
%T Towards a Taxonomy of Text Mining Features.
%U http://dblp.uni-trier.de/db/conf/ecis/ecis2019.html#FrommWS19
%X Recently, text mining has received special attention from both researchers and practitioners, since it enables the development of intelligent and automated services. Text mining has been influenced by different disciplines like computer science, statistics, computational linguistics and library and infor-mation sciences. However, text mining features that evolved in one particular discipline are often un-known or rarely used in the other disciplines. No scientific feature framework exits which facilitates costly feature engineering and evaluation. Therefore, we aim to develop a novel text mining feature taxonomy, which helps researchers and practitioners to develop, refine, compare and evaluate their text mining studies. In this research in progress paper, we focus on laying the foundation for our tax-onomy development by presenting our first two research cycles. Here, we were aiming for diversity, not completeness. We derived five dimensions and classified different text features accordingly to pro-vide a deeper understanding.
@inproceedings{conf/ecis/FrommWS19,
abstract = {Recently, text mining has received special attention from both researchers and practitioners, since it enables the development of intelligent and automated services. Text mining has been influenced by different disciplines like computer science, statistics, computational linguistics and library and infor-mation sciences. However, text mining features that evolved in one particular discipline are often un-known or rarely used in the other disciplines. No scientific feature framework exits which facilitates costly feature engineering and evaluation. Therefore, we aim to develop a novel text mining feature taxonomy, which helps researchers and practitioners to develop, refine, compare and evaluate their text mining studies. In this research in progress paper, we focus on laying the foundation for our tax-onomy development by presenting our first two research cycles. Here, we were aiming for diversity, not completeness. We derived five dimensions and classified different text features accordingly to pro-vide a deeper understanding. },
added-at = {2020-05-19T17:24:29.000+0200},
author = {Fromm, Hansjörg and Wambsganss, Thiemo and Söllner, Matthias},
biburl = {https://www.bibsonomy.org/bibtex/2ceb60c98380ca655a0e4eff35a3652cc/wise-kassel},
booktitle = {European Conference on Information Systems (ECIS) 2019},
crossref = {conf/ecis/2019},
editor = {vom Brocke, Jan and Gregor, Shirley and Müller, Oliver},
ee = {https://aisel.aisnet.org/ecis2019_rip/53},
interhash = {17792e2d87bf6eae550717f64a55ab2b},
intrahash = {ceb60c98380ca655a0e4eff35a3652cc},
keywords = {itegpub pub_msö pub_wise-kassel},
month = jun,
pages = 12,
publisher = {European Conference on Information Systems (ECIS)},
timestamp = {2020-05-19T17:24:29.000+0200},
title = {Towards a Taxonomy of Text Mining Features.},
url = {http://dblp.uni-trier.de/db/conf/ecis/ecis2019.html#FrommWS19},
year = 2019
}