Texty, a visualization tool to aid selection of texts from search outputs
Jaume Nualart, Faculty of Arts and Design, Univerity of Canberra & Machine Learning Research Group, NICTA. Australia
and Mario Pérez-Montoro, Department of Information Science, Faculty of Information Science. University of Barcelona, Spain
Introduction. The presentation of the results page in a search system plays an important role in satisfying the information needs of a user. The usual performance management criteria and tools to organise results have limitations that may hinder the satisfaction of those needs. We present Texty as a new approach that can help improve the search experience of users.
Method. The corpus of texts to which we applied Texty were papers from Information Research. To filter the texts we have build five groups of words or vocabularies on concrete fields of knowledge: conceptual approach, experimental approach, qualitative methodology, quantitative methodology and computers/IT.
Results. We show how Texty, intrinsically, is capable of encoding or offer its users information about the text that other alternative classic representations (bar or lines charts, mainly) are not able to offer.
Conclusions. Texty is a complementary tool that improves intellectual interaction with a lists of texts, allowing users to choose texts more effectively knowing their structure before reading them.
This is an Open Access publication: https://www.informationr.net/ir/18-2/paper581.html
Nualart, J. Pérez-Montoro, M (2013). Texty, a visualization tool to aid selection of texts from search outputs. Information Research, 18(2) paper 581. [Available at https://InformationR.net/ir/18-2/paper581.html]