Assessing text mining algorithm outcomes

Triss Ashton, Nicholas Evangelopoulos, Audhesh Paswan, Victor R. Prybutok and Robert Pavur

Journal of Business Analytics, 2020, vol. 3, issue 2, 107-121

Abstract: There is a surge in the development of decision-oriented analysis tools intended to extract actionable information from text. These tools integrate various text-mining methods that were performance tested in a manner that was often biased toward the new system. Those tests primarily utilised descriptive measurement criteria and test datasets that are inconsistent with most business corpora. We propose and test a user-oriented judgment approach that allows testing under controlled customer-oriented corpora and generates effect size measures. To illustrate the approach, customer relations data was analysed by latent semantic analysis and latent Dirichlet analysis with results evaluated by prospective business analysts. Reporting includes comparisons of results with published literature. While the research centres on the context-region text-mining systems, literature comparisons include word-embedding methods. The analysis concludes that none of the systems reviewed possess a repeatable statistical advantage over the others. Instead, distribution attributes, algorithm configuration, and the evaluation task drive results.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/2573234X.2020.1785342 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjbaxx:v:3:y:2020:i:2:p:107-121

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjba20

DOI: 10.1080/2573234X.2020.1785342

Access Statistics for this article

Journal of Business Analytics is currently edited by Dursan Delen

More articles in Journal of Business Analytics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().