Incorporating context in text analysis by interactive activation with competition artificial neural networks

Impact factor: outdated artefact or stepping-stone to journal certification?

“…All of these can be useful to identify important and pivotal material that may not be as conspicuous in a simple list of citations (Jahangiriana et al 2011). Other text analysis packages may offer complementary insights: for instance, Figure 5 is a perceptual map compiled from a Scopus search for 'journal impact factor' using CatPac (Woelfel 1993, Jörgensen 2005. Figure 5 makes it easy to note the juxtaposition of 'nursing' and 'peer review', of 'quality' and ISI (predecessor to Thomson Reuters), and of 'normailze' and 'journal impact factor', reflecting some of the issues canvassed in the literature.…”

“…Figure 5 makes it easy to note the juxtaposition of 'nursing' and 'peer review', of 'quality' and ISI (predecessor to Thomson Reuters), and of 'normailze' and 'journal impact factor', reflecting some of the issues canvassed in the literature. Catpac (Woelfel 1993, Jörgensen 2005, a text analysis system based on an artificial neural network, and ThoughtView (Woelfel and Woelfel 1997) allow the use more control to 'weed' the image of redundant detail to focus attention on salient features ( Figure 5). While such images assist discovery and the formulation of hypotheses, they are ill-suited for hypothesis testing, as the user has ample scope to influence the terms included in or omitted from the display.…”

Adopting neural net methodology for literature mapping and the generation of research ideas: An example at the interface of entrepreneurship, ICT, and economic development

“…and the modeler function of the software (Samkin & Schneider, 2008). CATPACII TM , on the other hand, is designed to imitate the functioning of human biology (Jo¨rgensen, 2005), which implies that it is capable of independent observation and learning. It is ''well suited for tasks a human being might do, but, for one reason or another, can't or won't do'' (Woelfel, 1998b).…”

Quantification of Transcripts from Depth Interviews, Open Ended Responses and Focus Groups: Challenges, Accomplishments, New Applications and Perspectives for Market Research

“…For instance, 'children', 'kids', 'siblings', 'daughters' are spelled differently although they are often used as synonyms with the purpose of making the conversation appear less monotonic. Sentence shifters like 'not' and 'never' also cause difficulties (Jörgensen 2005). Assume for a moment that homographs, synonyms and sentence shifters can be handled by the researcher through some manual work, and presume that plurals and other variants of a word can be conflated by a single term: before the final analysis can begin, the analyst will also need to discard 95% or more of the text body's words because they are of no relevance to the problem under scrutiny.…”