Reaction: Information Visualization for text analysis.

This is a reaction to one of the chapters in a textbook. This read talks about how visualization can be used as a medium for analyzing and understanding large collections of data. It actually thrives to make a point of the significance of visualizations in literary analysis by quoting umpteen examples. The author makes it very clear that most of the people working in the data analytics have not properly weighed the importance of visualizations as a search interface and I concur to the authors point on that.

This chapter of the textbook that we read, tried to explain data collections in view of analytics describing the relations,connections among different entities within the collection. It also talks about the relationships that exist between words in their usage language and in lexical ontologies.

The significance of this read is that it explains what factors are to be taken into consideration while working on information visualizations in text mining, document concordances, word frequencies, literature and citation relationships. Different examples quoted for explaining the visualizations in text mining were TAKMI, JigSaw, BETA systems. This read allowed me to gain understanding of how concordances can be made to provide data/information visualizations. This read talked about the SeeSoft and the TextArc visualizations in explaining how this is achieved. DocuBurst and the WordTree visualizations are the most commonly referred used techniques and this chapter talks about them too. The read also talks about the citation analysis graph and the ThemeRiver visualization.

This chapter is a comprehensive read of different visualization techniques to be used in text analysis and explains them by quoting different examples. Although examples for different systems were quoted this read failed to explain most of the important concepts in detail.