Tuesday, November 22, 2011

Reaction:Information Visualization for Text Analysis

This chapter is basically an overview of three different ideas for text visualization. One approach is text mining which is the representation of documents as a collection of entities and showing the connections between entities like in Jigsaw. In this paper, an example of mapping products with associated complaints received in a call center is provided. Such a representation would help identify which are the products having problems instead of having to skim through reports of the same. IBM's web fountain combines the tilebar approach with the document-entity relation approach and I find the results impressive. Also, in TRIST I liked the idea of representing each search result as a document icon. But with a search returning over hundreds of documents, the screen can appear clumsy affecting the readability.

The second approach is concordance analysis. Concordance is an index of all the words that appear in a text, showing those words in the contexts in which they appear. The words are alphabetically sorted. The examples presented under this section weren't easy to follow and there was too much information in the visualizations. In the DocuBurst visualization, the orientation of the words affected the readbaility. I found only the word tree visualization to be readable.

The third approach is to visualize relations between authors and citations. I find this technique useful in the field of research and see its applicability.But the sample graph created by Small does not appear to be very intuitive. I liked the paper lens visualization and found it easier to understand.