When one first hear the word screwing around, it makes them think, it makes them wonder. Yes, the word screwing around can have various meanings depending on the context in which it is used. Screwing around in this instance involves web browsing and looking for various bits of information, which to the user is rather uncertain at the time. London: Routledge, (2013), states that it involves one’s ability to collaborate and innovate, experimenting with data and working it against its grain, what Stephen Ramsay calls the ‘hermeneutics of screwing around. This to me now has new meaning, because I always saw it as plain old web browsing, from now on I shall call it “screwing around”, so next time a collegue or a friend call me up and ask what I’m doing, I would them reply “Screwing around”, just to see how they would react.
Many often ask, Why use text analysis? “Text analysis tools aide the interpreter asking questions of electronic texts.” “Text analysis practices encourage reflection on the
questions asked and formalization of queries.” “Text analysis is a way of targeting rereading that tests intuitions” (Geoffrey Rockwell).
The term data sets or text analysis, According to Christiane Nord (2005), in the book “Text Analysis in Translation” states, “most writer on translation theory agree that before embarking upon any translation the translator should analyse the text comprehensively, since this appears to be the only way of ensuring that the source text (ST) has been wholly and correctly understood (p.1).The term “Hermeneutics” as mentioned above in the term Screwing around”, is not a method in the same sense as Grounded Theory or content analysis but it deals with the entire framework of the process of analysis and interpretation and can therefore be stimulating and inspiring for the development of systematic qualitative text analysis Udo Kuckartz (2014).
In our session 7 of our DITA class, we were given once more time to “screw around” with three text JavaScript programs ,Wordle, Voyant and Many Eyes that analyzes texts. In doing this we used the data sets that we would have done from our session 6 class to painfully analyse. While our tutor Ernest and Ludi walked around assisting and aiding those in need of help.
The figure below shows JavaScript datasets for those with the most tweets
Datasets of Journal entries using Voyant.
References
Nord, C. 2005, Text analysis in translation: theory, methodology, and didactic application of a model for translation-oriented text analysis, Rodopi, Amsterdam.
http://0-srmo.sagepub.com.wam.city.ac.uk/view/qualitative-text-analysis/SAGE.xml
Libraries of the Future 