---
_id: '4691'
abstract:
- lang: eng
  text: Analysts have estimated that more than 80 percent of today’s data is stored
    in unstructured form (e.g., text, audio, image, video)—much of it expressed in
    rich and ambiguous natural language. Traditionally, to analyze natural language,
    one has used qualitative data-analysis approaches, such as manual coding. Yet,
    the size of text data sets obtained from the Internet makes manual analysis virtually
    impossible. In this tutorial, we discuss the challenges encountered when applying
    automated text-mining techniques in information systems research. In particular,
    we showcase how to use probabilistic topic modeling via Latent Dirichlet allocation,
    an unsupervised text-mining technique, with a LASSO multinomial logistic regression
    to explain user satisfaction with an IT artifact by automatically analyzing more
    than 12,000 online customer reviews. For fellow information systems researchers,
    this tutorial provides guidance for conducting text-mining studies on their own
    and for evaluating the quality of others.
author:
- first_name: Stefan
  full_name: Debortoli, Stefan
  last_name: Debortoli
- first_name: Oliver
  full_name: Müller, Oliver
  id: '72849'
  last_name: Müller
- first_name: Iris
  full_name: Junglas, Iris
  last_name: Junglas
- first_name: Jan
  full_name: vom Brocke, Jan
  last_name: vom Brocke
citation:
  ama: 'Debortoli S, Müller O, Junglas I, vom Brocke J. Text Mining for Information
    Systems Researchers: An Annotated Tutorial. <i>Communications of the Association
    for Information Systems</i>. Published online 2016:555-582. doi:<a href="https://doi.org/10.17705/1CAIS.03907">10.17705/1CAIS.03907</a>'
  apa: 'Debortoli, S., Müller, O., Junglas, I., &#38; vom Brocke, J. (2016). Text
    Mining for Information Systems Researchers: An Annotated Tutorial. <i>Communications
    of the Association for Information Systems</i>, 555–582. <a href="https://doi.org/10.17705/1CAIS.03907">https://doi.org/10.17705/1CAIS.03907</a>'
  bibtex: '@article{Debortoli_Müller_Junglas_vom Brocke_2016, title={Text Mining for
    Information Systems Researchers: An Annotated Tutorial}, DOI={<a href="https://doi.org/10.17705/1CAIS.03907">10.17705/1CAIS.03907</a>},
    journal={Communications of the Association for Information Systems}, author={Debortoli,
    Stefan and Müller, Oliver and Junglas, Iris and vom Brocke, Jan}, year={2016},
    pages={555–582} }'
  chicago: 'Debortoli, Stefan, Oliver Müller, Iris Junglas, and Jan vom Brocke. “Text
    Mining for Information Systems Researchers: An Annotated Tutorial.” <i>Communications
    of the Association for Information Systems</i>, 2016, 555–82. <a href="https://doi.org/10.17705/1CAIS.03907">https://doi.org/10.17705/1CAIS.03907</a>.'
  ieee: 'S. Debortoli, O. Müller, I. Junglas, and J. vom Brocke, “Text Mining for
    Information Systems Researchers: An Annotated Tutorial,” <i>Communications of
    the Association for Information Systems</i>, pp. 555–582, 2016, doi: <a href="https://doi.org/10.17705/1CAIS.03907">10.17705/1CAIS.03907</a>.'
  mla: 'Debortoli, Stefan, et al. “Text Mining for Information Systems Researchers:
    An Annotated Tutorial.” <i>Communications of the Association for Information Systems</i>,
    2016, pp. 555–82, doi:<a href="https://doi.org/10.17705/1CAIS.03907">10.17705/1CAIS.03907</a>.'
  short: S. Debortoli, O. Müller, I. Junglas, J. vom Brocke, Communications of the
    Association for Information Systems (2016) 555–582.
date_created: 2018-10-12T08:30:04Z
date_updated: 2026-03-10T09:44:54Z
department:
- _id: '196'
doi: 10.17705/1CAIS.03907
extern: '1'
keyword:
- Latent dirichlet allocation
- Online customer reviews
- Text mining
- Topic modeling
- User satisfaction
language:
- iso: eng
page: 555-582
publication: Communications of the Association for Information Systems
publication_identifier:
  isbn:
  - '9781615679119'
  issn:
  - 1529-3181
status: public
title: 'Text Mining for Information Systems Researchers: An Annotated Tutorial'
type: journal_article
user_id: '14972'
year: '2016'
...